Motor efficiency and degradation interpretation system

ABSTRACT

A method can include receiving real-time data during a drilling operation performed by a drillstring that includes a mud motor and a bit characterized by an expected performance profile; determining actual performance of the drillstring based at least in part on the real-time data; predicting degraded performance of the drillstring based at least in part on the real-time data and a mud motor degradation model; and updating the expected performance profile based on a comparison of the actual performance and the degraded performance.

RELATED APPLICATION

This application claims priority to and the benefit of a U.S. Provisional Application having Ser. No. 63/199071, filed 4 Dec. 2020 and entitled “Motor Efficiency and Degradation Interpretation Application”, which is incorporated by reference herein.

BACKGROUND

A reservoir can be a subsurface formation that can be characterized at least in part by its porosity and fluid permeability. As an example, a reservoir may be part of a basin such as a sedimentary basin. A basin can be a depression (e.g., caused by plate tectonic activity, subsidence, etc.) in which sediments accumulate. As an example, where hydrocarbon source rocks occur in combination with appropriate depth and duration of burial, a petroleum system may develop within a basin, which may form a reservoir that includes hydrocarbon fluids (e.g., oil, gas, etc.).

In oil and gas exploration, interpretation is a process that involves analysis of data to identify and locate various subsurface structures (e.g., horizons, faults, geobodies, etc.) in a geologic environment. Various types of structures (e.g., stratigraphic formations) may be indicative of hydrocarbon traps or flow channels, as may be associated with one or more reservoirs (e.g., fluid reservoirs). In the field of resource extraction, enhancements to interpretation can allow for construction of a more accurate model of a subsurface region, which, in turn, may improve characterization of the subsurface region for purposes of resource extraction. Characterization of one or more subsurface regions in a geologic environment can guide, for example, performance of one or more operations (e.g., field operations, etc.). As an example, a more accurate model of a subsurface region may make a drilling operation more accurate as to a borehole's trajectory where the borehole is to have a trajectory that penetrates a reservoir, etc., where fluid may be produced via the borehole (e.g., as a completed well, etc.). As an example, one or more workflows may be performed using one or more computational frameworks and/or one or more pieces of equipment that include features for one or more of analysis, acquisition, model building, control, etc., for exploration, interpretation, drilling, fracturing, production, etc.

SUMMARY

A method can include receiving real-time data during a drilling operation performed by a drillstring that includes a mud motor and a bit characterized by an expected performance profile; determining actual performance of the drillstring based at least in part on the real-time data; predicting degraded performance of the drillstring based at least in part on the real-time data and a mud motor degradation model; and updating the expected performance profile based on a comparison of the actual performance and the degraded performance. A system can include a processor; memory accessible to the processor; processor-executable instructions stored in the memory and executable by the processor to instruct the system to: receive real-time data during a drilling operation performed by a drillstring that includes a mud motor and a bit characterized by an expected performance profile; determine actual performance of the drillstring based at least in part on the real-time data; predict degraded performance of the drillstring based at least in part on the real-time data and a mud motor degradation model; and update the expected performance profile based on a comparison of the actual performance and the degraded performance. One or more computer-readable media can include computer-executable instructions executable by a system to instruct the system to: receive real-time data during a drilling operation performed by a drillstring that includes a mud motor and a bit characterized by an expected performance profile; determine actual performance of the drillstring based at least in part on the real-time data; predict degraded performance of the drillstring based at least in part on the real-time data and a mud motor degradation model; and update the expected performance profile based on a comparison of the actual performance and the degraded performance. Various other apparatuses, systems, methods, etc., are also disclosed.

This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the described implementations can be more readily understood by reference to the following description taken in conjunction with the accompanying drawings.

FIG. 1 illustrates an example system that includes various framework components associated with one or more geologic environments;

FIG. 2 illustrates examples of systems;

FIG. 3 illustrates an example of a system;

FIG. 4 illustrates an example of a system;

FIG. 5 illustrates an example of an environment and examples of equipment;

FIG. 6 illustrates an example of a system;

FIG. 7 illustrates an example of a graphical user interface;

FIG. 8 illustrates examples of graphical user interfaces;

FIG. 9 illustrates an example of a graphical user interface;

FIG. 10 illustrates examples of graphical user interfaces;

FIG. 11 illustrates an example of a graphical user interface;

FIG. 12 illustrates examples of graphical user interfaces;

FIG. 13 illustrates an example of a system;

FIG. 14 illustrates an example of a system and workflow;

FIG. 15 illustrates examples of graphical user interfaces;

FIG. 16 illustrates an example of a system and workflow;

FIG. 17 illustrates an example of a graphical user interface;

FIG. 18 illustrates examples of graphical user interfaces;

FIG. 19 illustrates examples of graphical user interfaces;

FIG. 20 illustrates an example of a graphical user interface;

FIG. 21 illustrates an example of a graphical user interface;

FIG. 22 illustrates an example of a graphical user interface;

FIG. 23 illustrates an example of a graphical user interface;

FIG. 24 illustrates examples of graphical user interfaces;

FIG. 25 illustrates examples of graphical user interfaces;

FIG. 26 illustrates an example of a graphical user interface;

FIG. 27 illustrates examples of graphical user interfaces;

FIG. 28 illustrates an example of a graphical user interface;

FIG. 29 illustrates an example of a graphical user interface;

FIG. 30 illustrates examples of graphical user interfaces;

FIG. 31 illustrates an example of a graphical user interface;

FIG. 32 illustrates examples of graphical user interfaces;

FIG. 33 illustrates an example of a graphical user interface;

FIG. 34 illustrates an example of a graphical user interface;

FIG. 35 illustrates an example of a graphical user interface;

FIG. 36 illustrates an example of a graphical user interface;

FIG. 37 illustrates an example of a graphical user interface;

FIG. 38 illustrates an example of a graphical user interface;

FIG. 39 illustrates an example of a graphical user interface;

FIG. 40 illustrates an example of a graphical user interface;

FIG. 41 illustrates an example of a graphical user interface;

FIG. 42 illustrates an example of a graphical user interface;

FIG. 43 illustrates an example of a graphical user interface;

FIG. 44 illustrates an example of a graphical user interface;

FIG. 45 illustrates an example of a graphical user interface;

FIG. 46 illustrates an example of a graphical user interface;

FIG. 47 illustrates examples of graphical user interfaces;

FIG. 48 illustrates examples of graphical user interfaces;

FIG. 49 illustrates an example of a graphical user interface;

FIG. 50 illustrates an example of a system and an example of a graphical user interface;

FIG. 51 illustrates an example of a graphical user interface;

FIG. 52 illustrates examples of graphical user interfaces;

FIG. 53 illustrates an example of a method and an example of a system;

FIG. 54 illustrates examples of computer and network equipment; and

FIG. 55 illustrates example components of a system and a networked system.

DETAILED DESCRIPTION

This description is not to be taken in a limiting sense, but rather is made merely for the purpose of describing the general principles of the implementations. The scope of the described implementations should be ascertained with reference to the issued claims.

FIG. 1 shows an example of a system 100 that includes a workspace framework 110 that can provide for instantiation of, rendering of, interactions with, etc., a graphical user interface (GUI) 120. In the example of FIG. 1 , the GUI 120 can include graphical controls for computational frameworks (e.g., applications) 121, projects 122, visualization 123, one or more other features 124, data access 125, and data storage 126.

In the example of FIG. 1 , the workspace framework 110 may be tailored to a particular geologic environment such as an example geologic environment 150. For example, the geologic environment 150 may include layers (e.g., stratification) that include a reservoir 151 and that may be intersected by a fault 153. As an example, the geologic environment 150 may be outfitted with a variety of sensors, detectors, actuators, etc. For example, equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 156 may be located remote from a wellsite and include sensing, detecting, emitting or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc. As an example, one or more satellites may be provided for purposes of communications, data acquisition, etc. For example, FIG. 1 shows a satellite in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or alternatively include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

FIG. 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc.). As an example, the equipment 157 and/or 158 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.

In the example of FIG. 1 , the GUI 120 shows some examples of computational frameworks, including the DRILLPLAN, PETREL, TECHLOG, PETROMOD, ECLIPSE, PIPESIM, and INTERSECT frameworks (Schlumberger Limited, Houston, Texas).

The DRILLPLAN framework provides for digital well construction planning and includes features for automation of repetitive tasks and validation workflows, enabling improved quality drilling programs (e.g., digital drilling plans, etc.) to be produced quickly with assured coherency.

The PETREL framework can be part of the DELFI cognitive E&P environment (Schlumberger Limited, Houston, Texas) for utilization in geosciences and geoengineering, for example, to analyze subsurface data from exploration to production of fluid from a reservoir.

The TECHLOG framework can handle and process field and laboratory data for a variety of geologic environments (e.g., deepwater exploration, shale, etc.). The TECHLOG framework can structure wellbore data for analyses, planning, etc.

The PETROMOD framework provides petroleum systems modeling capabilities that can combine one or more of seismic, well, and geological information to model the evolution of a sedimentary basin. The PETROMOD framework can predict if, and how, a reservoir has been charged with hydrocarbons, including the source and timing of hydrocarbon generation, migration routes, quantities, and hydrocarbon type in the subsurface or at surface conditions.

The ECLIPSE framework provides a reservoir simulator (e.g., as a computational framework) with numerical solutions for fast and accurate prediction of dynamic behavior for various types of reservoirs and development schemes.

The INTERSECT framework provides a high-resolution reservoir simulator for simulation of detailed geological features and quantification of uncertainties, for example, by creating accurate production scenarios and, with the integration of precise models of the surface facilities and field operations, the INTERSECT framework can produce reliable results, which may be continuously updated by real-time data exchanges (e.g., from one or more types of data acquisition equipment in the field that can acquire data during one or more types of field operations, etc.). The INTERSECT framework can provide completion configurations for complex wells where such configurations can be built in the field, can provide detailed chemical-enhanced-oil-recovery (EOR) formulations where such formulations can be implemented in the field, can analyze application of steam injection and other thermal EOR techniques for implementation in the field, advanced production controls in terms of reservoir coupling and flexible field management, and flexibility to script customized solutions for improved modeling and field management control. The INTERSECT framework, as with the other example frameworks, may be utilized as part of the DELFI cognitive E&P environment, for example, for rapid simulation of multiple concurrent cases. For example, a workflow may utilize one or more of the DELFI on demand reservoir simulation features.

The aforementioned DELFI environment provides various features for workflows as to subsurface analysis, planning, construction and production, for example, as illustrated in the workspace framework 110. Such an environment may be referred to as a process operations environment that can include a variety of frameworks (e.g., applications, etc.). As shown in FIG. 1 , outputs from the workspace framework 110 can be utilized for directing, controlling, etc., one or more processes in the geologic environment 150 and, feedback 160, can be received via one or more interfaces in one or more forms (e.g., acquired data as to operational conditions, equipment conditions, environment conditions, etc.).

As an example, a workflow may progress to a geology and geophysics (“G&G”) service provider, which may generate a well trajectory, which may involve execution of one or more G&G software packages. Examples of such software packages include the PETREL framework. As an example, a system or systems may utilize a framework such as the DELFI framework (Schlumberger Limited, Houston, Texas). Such a framework may operatively couple various other frameworks to provide for a multi-framework workspace. As an example, the GUI 120 of FIG. 1 may be a GUI of the DELFI framework.

In the example of FIG. 1 , the visualization features 123 may be implemented via the workspace framework 110, for example, to perform tasks as associated with one or more of subsurface regions, planning operations, constructing wells and/or surface fluid networks, and producing from a reservoir.

As an example, a visualization process can implement one or more of various features that can be suitable for one or more web applications. For example, a template may involve use of the JAVASCRIPT object notation format (JSON) and/or one or more other languages/formats. As an example, a framework may include one or more converters. For example, consider a JSON to PYTHON converter and/or a PYTHON to JSON converter. Such an approach can provide for compatibility of devices, frameworks, etc., with respect to one or more sets of instructions.

As an example, visualization features can provide for visualization of various earth models, properties, etc., in one or more dimensions. As an example, visualization features can provide for rendering of information in multiple dimensions, which may optionally include multiple resolution rendering. In such an example, information being rendered may be associated with one or more frameworks and/or one or more data stores. As an example, visualization features may include one or more control features for control of equipment, which can include, for example, field equipment that can perform one or more field operations. As an example, a workflow may utilize one or more frameworks to generate information that can be utilized to control one or more types of field equipment (e.g., drilling equipment, wireline equipment, fracturing equipment, etc.).

As to a reservoir model that may be suitable for utilization by a simulator, consider acquisition of seismic data as acquired via reflection seismology, which finds use in geophysics, for example, to estimate properties of subsurface formations. As an example, reflection seismology may provide seismic data representing waves of elastic energy (e.g., as transmitted by P-waves and S-waves, in a frequency range of approximately 1 Hz to approximately 100 Hz). Seismic data may be processed and interpreted, for example, to understand better composition, fluid content, extent and geometry of subsurface rocks. Such interpretation results can be utilized to plan, simulate, perform, etc., one or more operations for production of fluid from a reservoir (e.g., reservoir rock, etc.).

Field acquisition equipment may be utilized to acquire seismic data, which may be in the form of traces where a trace can include values organized with respect to time and/or depth (e.g., consider 1D, 2D, 3D or 4D seismic data). For example, consider acquisition equipment that acquires digital samples at a rate of one sample per approximately 4 ms. Given a speed of sound in a medium or media, a sample rate may be converted to an approximate distance. For example, the speed of sound in rock may be on the order of around 5 km per second. Thus, a sample time spacing of approximately 4 ms would correspond to a sample “depth” spacing of about 10 meters (e.g., assuming a path length from source to boundary and boundary to sensor). As an example, a trace may be about 4 seconds in duration; thus, for a sampling rate of one sample at about 4 ms intervals, such a trace would include about 1000 samples where latter acquired samples correspond to deeper reflection boundaries. If the 4 second trace duration of the foregoing example is divided by two (e.g., to account for reflection), for a vertically aligned source and sensor, a deepest boundary depth may be estimated to be about 10 km (e.g., assuming a speed of sound of about 5 km per second).

As an example, a model may be a simulated version of a geologic environment. As an example, a simulator may include features for simulating physical phenomena in a geologic environment based at least in part on a model or models. A simulator, such as a reservoir simulator, can simulate fluid flow in a geologic environment based at least in part on a model that can be generated via a framework that receives seismic data. A simulator can be a computerized system (e.g., a computing system) that can execute instructions using one or more processors to solve a system of equations that describe physical phenomena subject to various constraints. In such an example, the system of equations may be spatially defined (e.g., numerically discretized) according to a spatial model that that includes layers of rock, geobodies, etc., that have corresponding positions that can be based on interpretation of seismic and/or other data. A spatial model may be a cell-based model where cells are defined by a grid (e.g., a mesh). A cell in a cell-based model can represent a physical area or volume in a geologic environment where the cell can be assigned physical properties (e.g., permeability, fluid properties, etc.) that may be germane to one or more physical phenomena (e.g., fluid volume, fluid flow, pressure, etc.). A reservoir simulation model can be a spatial model that may be cell-based.

A simulator can be utilized to simulate the exploitation of a real reservoir, for example, to examine different productions scenarios to find an optimal one before production or further production occurs. A reservoir simulator does not provide an exact replica of flow in and production from a reservoir at least in part because the description of the reservoir and the boundary conditions for the equations for flow in a porous rock are generally known with an amount of uncertainty. Certain types of physical phenomena occur at a spatial scale that can be relatively small compared to size of a field. A balance can be struck between model scale and computational resources that results in model cell sizes being of the order of meters; rather than a lesser size (e.g., a level of detail of pores). A modeling and simulation workflow for multiphase flow in porous media (e.g., reservoir rock, etc.) can include generalizing real micro-scale data from macro scale observations (e.g., seismic data and well data) and upscaling to a manageable scale and problem size. Uncertainties can exist in input data and solution procedure such that simulation results too are to some extent uncertain. A process known as history matching can involve comparing simulation results to actual field data acquired during production of fluid from a field. Information gleaned from history matching, can provide for adjustments to a model, data, etc., which can help to increase accuracy of simulation.

As an example, a simulator may utilize various types of constructs, which may be referred to as entities. Entities may include earth entities or geological objects such as wells, surfaces, reservoirs, etc. Entities can include virtual representations of actual physical entities that may be reconstructed for purposes of simulation. Entities may include entities based on data acquired via sensing, observation, etc. (e.g., consider entities based at least in part on seismic data and/or other information). As an example, an entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property, etc.). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.

As an example, a simulator may utilize an object-based software framework, which may include entities based on pre-defined classes to facilitate modeling and simulation. As an example, an object class can encapsulate reusable code and associated data structures. Object classes can be used to instantiate object instances for use by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data. A model of a basin, a reservoir, etc. may include one or more boreholes where a borehole may be, for example, for measurements, injection, production, etc. As an example, a borehole may be a wellbore of a well, which may be a completed well (e.g., for production of a resource from a reservoir, for injection of material, etc.).

While several simulators are illustrated in the example of FIG. 1 , one or more other simulators may be utilized, additionally or alternatively. For example, consider the VISAGE geomechanics simulator (Schlumberger Limited, Houston Texas) or the PIPESIM network simulator (Schlumberger Limited, Houston Texas), etc. The VISAGE simulator includes finite element numerical solvers that may provide simulation results such as, for example, results as to compaction and subsidence of a geologic environment, well and completion integrity in a geologic environment, cap-rock and fault-seal integrity in a geologic environment, fracture behavior in a geologic environment, thermal recovery in a geologic environment, CO₂ disposal, etc. The PIPESIM simulator includes solvers that may provide simulation results such as, for example, multiphase flow results (e.g., from a reservoir to a wellhead and beyond, etc.), flowline and surface facility performance, etc. The PIPESIM simulator may be integrated, for example, with the AVOCET production operations framework (Schlumberger Limited, Houston Texas). As an example, a reservoir or reservoirs may be simulated with respect to one or more enhanced recovery techniques (e.g., consider a thermal process such as steam-assisted gravity drainage (SAGD), etc.). As an example, the PIPESIM simulator may be an optimizer that can optimize one or more operational scenarios at least in part via simulation of physical phenomena. The MANGROVE simulator (Schlumberger Limited, Houston, Texas) provides for optimization of stimulation design (e.g., stimulation treatment operations such as hydraulic fracturing) in a reservoir-centric environment. The MANGROVE framework can combine scientific and experimental work to predict geomechanical propagation of hydraulic fractures, reactivation of natural fractures, etc., along with production forecasts within 3D reservoir models (e.g., production from a drainage area of a reservoir where fluid moves via one or more types of fractures to a well and/or from a well). The MANGROVE framework can provide results pertaining to heterogeneous interactions between hydraulic and natural fracture networks, which may assist with optimization of the number and location of fracture treatment stages (e.g., stimulation treatment(s)), for example, to increased perforation efficiency and recovery.

The PETREL framework provides components that allow for optimization of exploration and development operations. The PETREL framework includes seismic to simulation software components that can output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) can develop collaborative workflows and integrate operations to streamline processes (e.g., with respect to one or more geologic environments, etc.). Such a framework may be considered an application (e.g., executable using one or more devices) and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).

As mentioned, a framework may be implemented within or in a manner operatively coupled to the DELFI cognitive exploration and production (E&P) environment (Schlumberger, Houston, Texas), which is a secure, cognitive, cloud-based collaborative environment that integrates data and workflows with digital technologies, such as artificial intelligence and machine learning. As an example, such an environment can provide for operations that involve one or more frameworks. The DELFI environment may be referred to as the DELFI framework, which may be a framework of frameworks. As an example, the DELFI framework can include various other frameworks, which can include, for example, one or more types of models (e.g., simulation models, etc.).

As an example, data can include geochemical data. For example, consider data acquired using X-ray fluorescence (XRF) technology, Fourier transform infrared spectroscopy (FTIR) technology and/or wireline geochemical technology.

As an example, one or more probes may be deployed in a bore via a wireline or wirelines. As an example, a probe may emit energy and receive energy where such energy may be analyzed to help determine mineral composition of rock surrounding a bore. As an example, nuclear magnetic resonance may be implemented (e.g., via a wireline, downhole NMR probe, etc.), for example, to acquire data as to nuclear magnetic properties of elements in a formation (e.g., hydrogen, carbon, phosphorous, etc.).

As an example, lithology scanning technology may be employed to acquire and analyze data. For example, consider the LITHO SCANNER technology marketed by Schlumberger Limited (Houston, Texas). As an example, a LITHO SCANNER tool may be a gamma ray spectroscopy tool.

As an example, a tool may be positioned to acquire information in a portion of a borehole. Analysis of such information may reveal vugs, dissolution planes (e.g., dissolution along bedding planes), stress-related features, dip events, etc. As an example, a tool may acquire information that may help to characterize a fractured reservoir, optionally where fractures may be natural and/or artificial (e.g., hydraulic fractures). Such information may assist with completions, stimulation treatment, etc. As an example, information acquired by a tool may be analyzed using a framework such as the aforementioned TECHLOG framework (Schlumberger Limited, Houston, Texas).

As an example, a workflow may utilize one or more types of data for one or more processes (e.g., stratigraphic modeling, basin modeling, completion designs, drilling, production, injection, etc.). As an example, one or more tools may provide data that can be used in a workflow or workflows that may implement one or more frameworks (e.g., PETREL, TECHLOG, PETROMOD, ECLIPSE, etc.).

FIG. 2 shows an example of a geologic environment 210 that includes reservoirs 211-1 and 211-2, which may be faulted by faults 212-1 and 212-2, an example of a network of equipment 230, an enlarged view of a portion of the network of equipment 230, referred to as network 240, and an example of a system 250. FIG. 2 shows some examples of offshore equipment 214 for oil and gas operations related to the reservoir 211-2 and onshore equipment 216 for oil and gas operations related to the reservoir 211-1.

In the example of FIG. 2 , the various equipment 214 and 216 can include drilling equipment, wireline equipment, production equipment, etc. For example, consider the equipment 214 as including a drilling rig that can drill into a formation to reach a reservoir target where a well can be completed for production of hydrocarbons. In such an example, one or more features of the system 100 of FIG. 1 may be utilized. For example, consider utilizing a drilling or well plan framework, a drilling execution framework, etc., to plan, execute, etc., one or more drilling operations.

In FIG. 2 , the network 240 can be an example of a relatively small production system network. As shown, the network 240 forms somewhat of a tree like structure where flowlines represent branches (e.g., segments) and junctions represent nodes. As shown in FIG. 2 , the network 240 provides for transportation of oil and gas fluids from well locations along flowlines interconnected at junctions with final delivery at a central processing facility.

In the example of FIG. 2 , various portions of the network 240 may include conduit. For example, consider a perspective view of a geologic environment that includes two conduits which may be a conduit to Man1 and a conduit to Man3 in the network 240.

As shown in FIG. 2 , the example system 250 includes one or more information storage devices 252, one or more computers 254, one or more networks 260 and instructions 270 (e.g., organized as one or more sets of instructions). As to the one or more computers 254, each computer may include one or more processors (e.g., or processing cores) 256 and memory 258 for storing the instructions 270 (e.g., one or more sets of instructions), for example, executable by at least one of the one or more processors. As an example, a computer may include one or more network interfaces (e.g., wired or wireless), one or more graphics cards, a display interface (e.g., wired or wireless), etc. As an example, imagery such as surface imagery (e.g., satellite, geological, geophysical, etc.) may be stored, processed, communicated, etc. As an example, data may include SAR data, GPS data, etc. and may be stored, for example, in one or more of the storage devices 252. As an example, information that may be stored in one or more of the storage devices 252 may include information about equipment, location of equipment, orientation of equipment, fluid characteristics, etc.

As an example, the instructions 270 can include instructions (e.g., stored in the memory 258) executable by at least one of the one or more processors 256 to instruct the system 250 to perform various actions. As an example, the system 250 may be configured such that the instructions 270 provide for establishing a framework, for example, that can perform network modeling (see, e.g., the PIPESIM framework of the example of FIG. 1 , etc.). As an example, one or more methods, techniques, etc. may be performed using one or more sets of instructions, which may be, for example, the instructions 270 of FIG. 2 .

FIG. 3 shows an example of a wellsite system 300 (e.g., at a wellsite that may be onshore or offshore). As shown, the wellsite system 300 can include a mud tank 301 for holding mud and other material (e.g., where mud can be a drilling fluid), a suction line 303 that serves as an inlet to a mud pump 304 for pumping mud from the mud tank 301 such that mud flows to a vibrating hose 306, a drawworks 307 for winching drill line or drill lines 312, a standpipe 308 that receives mud from the vibrating hose 306, a kelly hose 309 that receives mud from the standpipe 308, a gooseneck or goosenecks 310, a traveling block 311, a crown block 313 for carrying the traveling block 311 via the drill line or drill lines 312, a derrick 314, a kelly 318 or a top drive 340, a kelly drive bushing 319, a rotary table 320, a drill floor 321, a bell nipple 322, one or more blowout preventors (BOPs) 323, a drillstring 325, a drill bit 326, a casing head 327 and a flow pipe 328 that carries mud and other material to, for example, the mud tank 301.

In the example system of FIG. 3 , a borehole 332 is formed in subsurface formations 330 by rotary drilling; noting that various example embodiments may also use one or more directional drilling techniques, equipment, etc.

As shown in the example of FIG. 3 , the drillstring 325 is suspended within the borehole 332 and has a drillstring assembly 350 that includes the drill bit 326 at its lower end. As an example, the drillstring assembly 350 may be a bottom hole assembly (BHA).

The wellsite system 300 can provide for operation of the drillstring 325 and other operations. As shown, the wellsite system 300 includes the traveling block 311 and the derrick 314 positioned over the borehole 332. As mentioned, the wellsite system 300 can include the rotary table 320 where the drillstring 325 pass through an opening in the rotary table 320.

As shown in the example of FIG. 3 , the wellsite system 300 can include the kelly 318 and associated components, etc., or the top drive 340 and associated components. As to a kelly example, the kelly 318 may be a square or hexagonal metal/alloy bar with a hole drilled therein that serves as a mud flow path. The kelly 318 can be used to transmit rotary motion from the rotary table 320 via the kelly drive bushing 319 to the drillstring 325, while allowing the drillstring 325 to be lowered or raised during rotation. The kelly 318 can pass through the kelly drive bushing 319, which can be driven by the rotary table 320. As an example, the rotary table 320 can include a master bushing that operatively couples to the kelly drive bushing 319 such that rotation of the rotary table 320 can turn the kelly drive bushing 319 and hence the kelly 318. The kelly drive bushing 319 can include an inside profile matching an outside profile (e.g., square, hexagonal, etc.) of the kelly 318; however, with slightly larger dimensions so that the kelly 318 can freely move up and down inside the kelly drive bushing 319.

As to a top drive example, the top drive 340 can provide functions performed by a kelly and a rotary table. The top drive 340 can turn the drillstring 325. As an example, the top drive 340 can include one or more motors (e.g., electric and/or hydraulic) connected with appropriate gearing to a short section of pipe called a quill, that in turn may be screwed into a saver sub or the drillstring 325 itself. The top drive 340 can be suspended from the traveling block 311, so the rotary mechanism is free to travel up and down the derrick 314. As an example, a top drive 340 may allow for drilling to be performed with more joint stands than a kelly/rotary table approach.

In the example of FIG. 3 , the mud tank 301 can hold mud, which can be one or more types of drilling fluids. As an example, a wellbore may be drilled to produce fluid, inject fluid or both (e.g., hydrocarbons, minerals, water, etc.).

In the example of FIG. 3 , the drillstring 325 (e.g., including one or more downhole tools) may be composed of a series of pipes threadably connected together to form a long tube with the drill bit 326 at the lower end thereof. As the drillstring 325 is advanced into a wellbore for drilling, at some point in time prior to or coincident with drilling, the mud may be pumped by the pump 304 from the mud tank 301 (e.g., or other source) via a the lines 306, 308 and 309 to a port of the kelly 318 or, for example, to a port of the top drive 340. The mud can then flow via a passage (e.g., or passages) in the drillstring 325 and out of ports located on the drill bit 326 (see, e.g., a directional arrow). As the mud exits the drillstring 325 via ports in the drill bit 326, it can then circulate upwardly through an annular region between an outer surface(s) of the drillstring 325 and surrounding wall(s) (e.g., open borehole, casing, etc.), as indicated by directional arrows. In such a manner, the mud lubricates the drill bit 326 and carries heat energy (e.g., frictional or other energy) and formation cuttings to the surface where the mud (e.g., and cuttings) may be returned to the mud tank 301, for example, for recirculation (e.g., with processing to remove cuttings, etc.).

The mud pumped by the pump 304 into the drillstring 325 may, after exiting the drillstring 325, form a mudcake that lines the wellbore which, among other functions, may reduce friction between the drillstring 325 and surrounding wall(s) (e.g., borehole, casing, etc.). A reduction in friction may facilitate advancing or retracting the drillstring 325. During a drilling operation, the entire drillstring 325 may be pulled from a wellbore and optionally replaced, for example, with a new or sharpened drill bit, a smaller diameter drillstring, etc. As mentioned, the act of pulling a drillstring out of a hole or replacing it in a hole is referred to as tripping. A trip may be referred to as an upward trip or an outward trip or as a downward trip or an inward trip depending on trip direction.

As an example, consider a downward trip where upon arrival of the drill bit 326 of the drillstring 325 at a bottom of a wellbore, pumping of the mud commences to lubricate the drill bit 326 for purposes of drilling to enlarge the wellbore. As mentioned, the mud can be pumped by the pump 304 into a passage of the drillstring 325 and, upon filling of the passage, the mud may be used as a transmission medium to transmit energy, for example, energy that may encode information as in mud-pulse telemetry.

As an example, mud-pulse telemetry equipment may include a downhole device configured to effect changes in pressure in the mud to create an acoustic wave or waves upon which information may modulated. In such an example, information from downhole equipment (e.g., one or more modules of the drillstring 325) may be transmitted uphole to an uphole device, which may relay such information to other equipment for processing, control, etc.

As an example, telemetry equipment may operate via transmission of energy via the drillstring 325 itself. For example, consider a signal generator that imparts coded energy signals to the drillstring 325 and repeaters that may receive such energy and repeat it to further transmit the coded energy signals (e.g., information, etc.).

As an example, the drillstring 325 may be fitted with telemetry equipment 352 that includes a rotatable drive shaft, a turbine impeller mechanically coupled to the drive shaft such that the mud can cause the turbine impeller to rotate, a modulator rotor mechanically coupled to the drive shaft such that rotation of the turbine impeller causes said modulator rotor to rotate, a modulator stator mounted adjacent to or proximate to the modulator rotor such that rotation of the modulator rotor relative to the modulator stator creates pressure pulses in the mud, and a controllable brake for selectively braking rotation of the modulator rotor to modulate pressure pulses. In such example, an alternator may be coupled to the aforementioned drive shaft where the alternator includes at least one stator winding electrically coupled to a control circuit to selectively short the at least one stator winding to electromagnetically brake the alternator and thereby selectively brake rotation of the modulator rotor to modulate the pressure pulses in the mud.

In the example of FIG. 3 , an uphole control and/or data acquisition system 362 may include circuitry to sense pressure pulses generated by telemetry equipment 352 and, for example, communicate sensed pressure pulses or information derived therefrom for process, control, etc.

The assembly 350 of the illustrated example includes a logging-while-drilling (LWD) module 354, a measurement-while-drilling (MWD) module 356, an optional module 358, a rotary-steerable system (RSS) and/or motor 360, and the drill bit 326. Such components or modules may be referred to as tools where a drillstring can include a plurality of tools.

As to a RSS, it involves technology utilized for directional drilling. Directional drilling involves drilling into the Earth to form a deviated bore such that the trajectory of the bore is not vertical; rather, the trajectory deviates from vertical along one or more portions of the bore. As an example, consider a target that is located at a lateral distance from a surface location where a rig may be stationed. In such an example, drilling can commence with a vertical portion and then deviate from vertical such that the bore is aimed at the target and, eventually, reaches the target. Directional drilling may be implemented where a target may be inaccessible from a vertical location at the surface of the Earth, where material exists in the Earth that may impede drilling or otherwise be detrimental (e.g., consider a salt dome, etc.), where a formation is laterally extensive (e.g., consider a relatively thin yet laterally extensive reservoir), where multiple bores are to be drilled from a single surface bore, where a relief well is desired, etc.

One approach to directional drilling involves a mud motor; however, a mud motor can present some challenges depending on factors such as rate of penetration (ROP), transferring weight to a bit (e.g., weight on bit, WOB) due to friction, etc. A mud motor can be a positive displacement motor (PDM) that operates to drive a bit (e.g., during directional drilling, etc.). A PDM operates as drilling fluid is pumped through it where the PDM converts hydraulic power of the drilling fluid into mechanical power to cause the bit to rotate.

As an example, a PDM may operate in a combined rotating mode where surface equipment is utilized to rotate a bit of a drillstring (e.g., a rotary table, a top drive, etc.) by rotating the entire drillstring and where drilling fluid is utilized to rotate the bit of the drillstring. In such an example, a surface RPM (SRPM) may be determined by use of the surface equipment and a downhole RPM of the mud motor may be determined using various factors related to flow of drilling fluid, mud motor type, etc. As an example, in the combined rotating mode, bit RPM can be determined or estimated as a sum of the SRPM and the mud motor RPM, assuming the SRPM and the mud motor RPM are in the same direction.

As an example, a PDM mud motor can operate in a so-called sliding mode, when the drillstring is not rotated from the surface. In such an example, a bit RPM can be determined or estimated based on the RPM of the mud motor.

A RSS can drill directionally where there is continuous rotation from surface equipment, which can alleviate the sliding of a steerable motor (e.g., a PDM). A RSS may be deployed when drilling directionally (e.g., deviated, horizontal, or extended-reach wells). A RSS can aim to minimize interaction with a borehole wall, which can help to preserve borehole quality. A RSS can aim to exert a relatively consistent side force akin to stabilizers that rotate with the drillstring or orient the bit in the desired direction while continuously rotating at the same number of rotations per minute as the drillstring.

The LWD module 354 may be housed in a suitable type of drill collar and can contain one or a plurality of selected types of logging tools. It will also be understood that more than one LWD and/or MWD module can be employed, for example, as represented at by the module 356 of the drillstring assembly 350. Where the position of an LWD module is mentioned, as an example, it may refer to a module at the position of the LWD module 354, the module 356, etc. An LWD module can include capabilities for measuring, processing, and storing information, as well as for communicating with the surface equipment. In the illustrated example, the LWD module 354 may include a seismic measuring device.

The MWD module 356 may be housed in a suitable type of drill collar and can contain one or more devices for measuring characteristics of the drillstring 325 and the drill bit 326. As an example, the MWD tool 354 may include equipment for generating electrical power, for example, to power various components of the drillstring 325. As an example, the MWD tool 354 may include the telemetry equipment 352, for example, where the turbine impeller can generate power by flow of the mud; it being understood that other power and/or battery systems may be employed for purposes of powering various components. As an example, the MWD module 356 may include one or more of the following types of measuring devices: a weight-on-bit measuring device, a torque measuring device, a vibration measuring device, a shock measuring device, a stick slip measuring device, a direction measuring device, and an inclination measuring device.

FIG. 3 also shows some examples of types of holes that may be drilled. For example, consider a slant hole 372, an S-shaped hole 374, a deep inclined hole 376 and a horizontal hole 378.

As an example, a drilling operation can include directional drilling where, for example, at least a portion of a well includes a curved axis. For example, consider a radius that defines curvature where an inclination with regard to the vertical may vary until reaching an angle between about 30 degrees and about 60 degrees or, for example, an angle to about 90 degrees or possibly greater than about 90 degrees.

As an example, a directional well can include several shapes where each of the shapes may aim to meet particular operational demands. As an example, a drilling process may be performed on the basis of information as and when it is relayed to a drilling engineer. As an example, inclination and/or direction may be modified based on information received during a drilling process.

As an example, deviation of a bore may be accomplished in part by use of a downhole motor and/or a turbine. As to a motor, for example, a drillstring can include a positive displacement motor (PDM).

As an example, a system may be a steerable system and include equipment to perform method such as geosteering. As mentioned, a steerable system can be or include an RSS. As an example, a steerable system can include a PDM or of a turbine on a lower part of a drillstring which, just above a drill bit, a bent sub can be mounted. As an example, above a PDM, MWD equipment that provides real time or near real time data of interest (e.g., inclination, direction, pressure, temperature, real weight on the drill bit, torque stress, etc.) and/or LWD equipment may be installed. As to the latter, LWD equipment can make it possible to send to the surface various types of data of interest, including for example, geological data (e.g., gamma ray log, resistivity, density and sonic logs, etc.).

The coupling of sensors providing information on the course of a well trajectory, in real time or near real time, with, for example, one or more logs characterizing the formations from a geological viewpoint, can allow for implementing a geosteering method. Such a method can include navigating a subsurface environment, for example, to follow a desired route to reach a desired target or targets.

As an example, a drillstring can include an azimuthal density neutron (ADN) tool for measuring density and porosity; a MWD tool for measuring inclination, azimuth and shocks; a compensated dual resistivity (CDR) tool for measuring resistivity and gamma ray related phenomena; one or more variable gauge stabilizers; one or more bend joints; and a geosteering tool, which may include a motor and optionally equipment for measuring and/or responding to one or more of inclination, resistivity and gamma ray related phenomena.

As an example, geosteering can include intentional directional control of a wellbore based on results of downhole geological logging measurements in a manner that aims to keep a directional wellbore within a desired region, zone (e.g., a pay zone), etc. As an example, geosteering may include directing a wellbore to keep the wellbore in a particular section of a reservoir, for example, to minimize gas and/or water breakthrough and, for example, to maximize economic production from a well that includes the wellbore.

Referring again to FIG. 3 , the wellsite system 300 can include one or more sensors 364 that are operatively coupled to the control and/or data acquisition system 362. As an example, a sensor or sensors may be at surface locations. As an example, a sensor or sensors may be at downhole locations. As an example, a sensor or sensors may be at one or more remote locations that are not within a distance of the order of about one hundred meters from the wellsite system 300. As an example, a sensor or sensor may be at an offset wellsite where the wellsite system 300 and the offset wellsite are in a common field (e.g., oil and/or gas field).

As an example, one or more of the sensors 364 can be provided for tracking pipe, tracking movement of at least a portion of a drillstring, etc.

As an example, the system 300 can include one or more sensors 366 that can sense and/or transmit signals to a fluid conduit such as a drilling fluid conduit (e.g., a drilling mud conduit). For example, in the system 300, the one or more sensors 366 can be operatively coupled to portions of the standpipe 308 through which mud flows. As an example, a downhole tool can generate pulses that can travel through the mud and be sensed by one or more of the one or more sensors 366. In such an example, the downhole tool can include associated circuitry such as, for example, encoding circuitry that can encode signals, for example, to reduce demands as to transmission. As an example, circuitry at the surface may include decoding circuitry to decode encoded information transmitted at least in part via mud-pulse telemetry. As an example, circuitry at the surface may include encoder circuitry and/or decoder circuitry and circuitry downhole may include encoder circuitry and/or decoder circuitry. As an example, the system 300 can include a transmitter that can generate signals that can be transmitted downhole via mud (e.g., drilling fluid) as a transmission medium.

As an example, one or more portions of a drillstring may become stuck. The term stuck can refer to one or more of varying degrees of inability to move or remove a drillstring from a bore. As an example, in a stuck condition, it might be possible to rotate pipe or lower it back into a bore or, for example, in a stuck condition, there may be an inability to move the drillstring axially in the bore, though some amount of rotation may be possible. As an example, in a stuck condition, there may be an inability to move at least a portion of the drillstring axially and rotationally.

As to the term “stuck pipe”, this can refer to a portion of a drillstring that cannot be rotated or moved axially. As an example, a condition referred to as “differential sticking” can be a condition whereby the drillstring cannot be moved (e.g., rotated or reciprocated) along the axis of the bore. Differential sticking may occur when high-contact forces caused by low reservoir pressures, high wellbore pressures, or both, are exerted over a sufficiently large area of the drillstring. Differential sticking can have time and financial cost.

As an example, a sticking force can be a product of the differential pressure between the wellbore and the reservoir and the area that the differential pressure is acting upon. This means that a relatively low differential pressure (delta p) applied over a large working area can be just as effective in sticking pipe as can a high differential pressure applied over a small area.

As an example, a condition referred to as “mechanical sticking” can be a condition where limiting or prevention of motion of the drillstring by a mechanism other than differential pressure sticking occurs. Mechanical sticking can be caused, for example, by one or more of junk in the hole, wellbore geometry anomalies, cement, keyseats or a buildup of cuttings in the annulus.

FIG. 4 shows an example of a wellsite system 400, specifically, FIG. 4 shows the wellsite system 400 in an approximate side view and an approximate plan view along with a block diagram of a system 470.

In the example of FIG. 4 , the wellsite system 400 can include a cabin 410, a rotary table 422, drawworks 424, a mast 426 (e.g., optionally carrying a top drive, etc.), mud tanks 430 (e.g., with one or more pumps, one or more shakers, etc.), one or more pump buildings 440, a boiler building 442, an HPU building 444 (e.g., with a rig fuel tank, etc.), a combination building 448 (e.g., with one or more generators, etc.), pipe tubs 462, a catwalk 464, a flare 468, etc. Such equipment can include one or more associated functions and/or one or more associated operational risks, which may be risks as to time, resources, and/or humans.

As shown in the example of FIG. 4 , the wellsite system 400 can include a system 470 that includes one or more processors 472, memory 474 operatively coupled to at least one of the one or more processors 472, instructions 476 that can be, for example, stored in the memory 474, and one or more interfaces 478. As an example, the system 470 can include one or more processor-readable media that include processor-executable instructions executable by at least one of the one or more processors 472 to cause the system 470 to control one or more aspects of the wellsite system 400. In such an example, the memory 474 can be or include the one or more processor-readable media where the processor-executable instructions can be or include instructions. As an example, a processor-readable medium can be a computer-readable storage medium that is not a signal and that is not a carrier wave.

FIG. 4 also shows a battery 480 that may be operatively coupled to the system 470, for example, to power the system 470. As an example, the battery 480 may be a back-up battery that operates when another power supply is unavailable for powering the system 470. As an example, the battery 480 may be operatively coupled to a network, which may be a cloud network. As an example, the battery 480 can include smart battery circuitry and may be operatively coupled to one or more pieces of equipment via a SMBus or other type of bus.

In the example of FIG. 4 , services 490 are shown as being available, for example, via a cloud platform. Such services can include data services 492, query services 494 and drilling services 496. As an example, the services 490 may be part of a system such as the system 300 of FIG. 3 .

As an example, the system 470 may be utilized to generate one or more rate of penetration drilling parameter values, which may, for example, be utilized to control one or more drilling operations.

As explained, drilling operations may utilize positive displacement motors (PDMs), which can be operationally more effective than rotary drilling alone. Various drilling operations may still use a bit and motor combination to drill a curved portion and a lateral portion of a wellbore where it can be frequently observed via downhole measurements that an average bit RPM decreases over time without a substantial change in surface parameters. As a mud motor provides additional energy to a system, such a decrease in RPM indicates some deterioration of motor efficiency. If such changes continue, it will likely entail worsening drilling dynamics and surface/downhole equipment failures. Estimating energy and efficiency of a mud motor based on available data can provide for monitoring the status of a mud motor.

FIG. 5 shows an example of a drilling assembly 500 in a geologic environment 501 that includes a borehole 503 where the drilling assembly 500 (e.g., a drillstring) includes a bit 504 and a motor section 510 where the motor section 510 can drive the bit 504 (e.g., cause the bit 504 to rotate and deepen the borehole 503).

As shown, the motor section 500 includes a dump valve 512, a power section 514, a surface-adjustable bent housing 516, a transmission assembly 518, a bearing section 520 and a drive shaft 522, which can be operatively coupled to a bit such as the bit 504.

As to the power section 514, two examples are illustrated as a power section 514-1 and a power section 514-2 each of which includes a housing 542, a rotor 544 and a stator 546. The rotor 544 and the stator 546 can be characterized by a ratio. For example, the power section 514-1 can be a 5:6 ratio and the power section 514-2 can be a 1:2 ratio, which, as seen in cross-sectional views, can involve lobes (e.g., a rotor/stator lobe configuration). The motor section 510 of FIG. 5 may be a POWERPAK family motor section (Schlumberger Limited, Houston, Texas) or another type of motor section. The POWERPAK family of motor sections can include ratios of 1:2, 2:3, 3:4, 4:5, 5:6 and 7:8 with corresponding lobe configurations.

A power section can convert hydraulic energy from drilling fluid into mechanical power to turn a bit. For example, consider the reverse application of the Moineau pump principle. During operation, drilling fluid can be pumped into a power section at a pressure that causes the rotor to rotate within the stator where the rotational force is transmitted through a transmission shaft and drive shaft to a bit.

A motor section may be manufactured in part of corrosion-resistant stainless steel where a thin layer of chrome plating may be present to reduce friction and abrasion. As an example, tungsten carbide may be utilized to coat a rotor, for example, to reduce abrasion wear and corrosion damage. As to a stator, it can be formed of a steel tube, which may be a housing (see, e.g., the housing 542) with an elastomeric material that lines the bore of the steel tube to define a stator. An elastomeric material may be referred to as a liner or, when assembled with the tube or housing, may be referred to as a stator. As an example, an elastomeric material may be molded into the bore of a tube. An elastomeric material can be formulated to resist abrasion and hydrocarbon induced deterioration. Various types of elastomeric materials may be utilized in a power section and some may be proprietary. Properties of an elastomeric material can be tailored for particular types of operations, which may consider factors such as temperature, speed, rotor type, type of drilling fluid, etc. Rotors and stators can be characterized by helical profiles, for example, by spirals and/or lobes. A rotor can have one less fewer spiral or lobe than a stator (see, e.g., the cross-sectional views in FIG. 5 ).

During operation, the rotor and stator can form a continuous seal at their contact points along a straight line, which produces a number of independent cavities. As fluid is forced through these progressive cavities, it causes the rotor to rotate inside the stator. The movement of the rotor inside the stator is referred to as nutation. For each nutation cycle, the rotor rotates by a distance of one lobe width. The rotor nutates each lobe in the stator to complete one revolution of the bit box. For example, a motor section with a 7:8 rotor/stator lobe configuration and a speed of 100 RPM at the bit box will have a nutation speed of 700 cycles per minute. Generally, torque output increases with the number of lobes, which corresponds to a slower speed. Torque also depends on the number of stages where a stage is a complete spiral of a stator helix. Power is defined as speed times torque; however, a greater number of lobes in a motor does not necessarily mean that the motor produces more power. Motors with more lobes tend to be less efficient because the seal area between the rotor and the stator increases with the number of lobes.

The difference between the size of a rotor mean diameter (e.g., valley to lobe peak measurement) and the stator minor diameter (lobe peak to lobe peak) is defined as the rotor/stator interference fit. Various motors are assembled with a rotor sized to be larger than a stator internal bore under planned downhole conditions, which can produce a strong positive interference seal that is referred to as a positive fit. Where higher downhole temperatures are expected, a positive fit can be reduced during motor assembly to allow for swelling of an elastomeric material that forms the stator (e.g., stator liner). Mud weight and vertical depth can be considered as they can influence the hydrostatic pressure on the stator liner. A computational framework such as, for example, the POWERFIT framework (Schlumberger Limited, Houston, Texas), may be utilized to calculate a desired interference fit.

As to some examples of elastomeric materials, consider nitrile rubber, which tends to be rated to approximately 138 C (280 F), and highly saturated nitrile, which may be formulated to resist chemical attack and be rated to approximately 177 C (350 F).

The spiral stage length of a stator is defined as the axial length for one lobe in the stator to rotate 360 degrees along its helical path around the body of the stator. The stage length of a rotor differs from that of a stator as a rotor has a shorter stage length than its corresponding stator. More stages can increase the number of fluid cavities in a power section, which can result in a greater total pressure drop. Under the same differential pressure conditions, the power section with more stages tends to maintain speed better as there tends to be less pressure drop per stage and hence less leakage.

Drilling fluid temperature, which may be referred to as mud temperature or mud fluid temperature, can be a factor in determining an amount of interference in assembling a stator and a rotor of a power section. As to interference, greater interference can result in a stator experiencing higher shearing stresses, which can cause fatigue damage. Fatigue can lead to premature chunking failure of a stator liner. As an example, chlorides or other such halides may cause damage to a power section. For example, such halides may damage a rotor through corrosion where a rough edged rotor can cut into a stator liner (e.g., cutting the top off an elastomeric liner). Such cuts can reduce effectiveness of a rotor/stator seal and may cause a motor to stall (e.g., chunking the stator) at a low differential pressure. For oil-based mud (OBM) with supersaturated water phases and for salt muds, a coated rotor can be beneficial.

As to differential pressure, it is defined as the difference between the on-bottom and off-bottom drilling pressure, which is generated by the rotor/stator section (power section) of a motor. As mentioned, for a larger pressure difference, there tends to be higher torque output and lower shaft speed. A motor that is run with differential pressures greater than recommended can be more prone to premature chunking. Such chunking may follow a spiral path or be uniform through the stator liner. A life of a power section can depend on factors that can lead to chunking (e.g., damage to a stator), which may depend on characteristics of a rotor (e.g., surface characteristics, etc.).

As to trajectory of a wellbore to be drilled, it can be defined in part by one or more dogleg severities (DLSs). Rotating a motor in high DLS interval of a well can increase risk of damage to a stator. For example, the geometry of a wellbore can cause a motor section to bend and flex. A power section stator can be relatively more flexible that other parts of a motor. Where the stator housing bends, the elastomeric liner can be biased or pushed upon by the housing, which can result in force being applied by the elastomeric liner to the rotor. Such force can lead to excessive compression on the stator lobes and cause chunking.

A motor can have a power curve. A test can be performed using a dynamo meter in a laboratory, for example, using water at room temperature to determine a relationship between input, which is flow rate and differential pressure, to power output, in the form of RPM and torque. Such information can be available in a motor handbook. However, what is actually happening downhole can differ due to various factors. For example, due to effect of downhole pressure and temperature, output can be reduced (e.g., the motor power output). Such a reduction may lead one to conclude that a motor is not performing. In response, a driller may keep pushing such that the pressure becomes too high, which can damage elastomeric material due to stalling (e.g., damage a stator).

As an example, power can be reduced downhole due to effects of temperature and pressure and/or one or more other factors. For example, consider a plot of power versus differential pressure where differences between surface and downhole may increase with higher differential pressures.

As to damage to an elastomeric material of a stator of a motor, separation from a tube or housing may occur in association with chunking.

FIG. 6 shows an example of a schematic of a system 600 that includes inputs that can be accounted for by a motor engine (see, e.g., the motor engine 930). As shown, information may be input from various sources. As an example, one or more filters may be available to, for example, filter by borehole size, filter by inventory (e.g., business system), filter by offset well analysis, etc.

As an example, a method can provide for evaluating mud motor energy and efficiency and, for example, estimating mud motor degradation. In such an example, with surface drilling parameters and downhole rotational speed synchronized, flow rate, mud motor rotational speed and differential pressure can be obtained from measured data. By utilizing mud motor power characteristic curves, mud motor torque and power output can be calculated. In such an example, this allows for computing mud motor efficiency from a ratio of motor power input to power output. As an example, a method can include using an equation derived to incorporate mud motor off bottom pressure to calculate motor efficiency. As an example, a change rate of motor efficiency can be obtained by fitting time-based and/or depth-based efficiency results.

As an example, a method can include generating a mud motor degradation indicator that provides an estimate as to how much a mud motor has degraded over time. In such an example, the degradation indicator can be based on calculating a difference between measured mud motor rotational speed and nominal rotational speed based on a motor characteristic curve. By fitting of a calculated degradation indicator over drilling time and/or drilling depth, a degradation rate can be estimated, for example, as to one or more future times, future depths, future drilling runs, etc.

As explained, a mud motor can be characterized by various parameters and/or conditions. One manner of characterization is a motor characteristic curve. Various approaches may be utilized to obtain a motor characteristic curve or curves. For example, consider use of a mud motor specification library where pre-defined mud motor curves are stored. Another approach can utilize a mud motor engine, which may be trained using machine learning, for example, based on mud motor power section finite element analysis (FEA) modeling results. With mud motor torque, differential pressure and power output being available, as an example, a method can include calculating torque rating, differential pressure rating and power rating, which may be used to evaluate whether a motor is working at the optimum zone.

As explained, a simulator may be utilized to compute a downhole power curve, optionally along with fatigue life. Such a simulator can model the geometry of the motor power section, and combine FEA and computational fluid dynamics (CFD) computation, optionally together with lab tests for elastomeric materials. As an example, computing clusters can run simulations to generate a simulation results database. Based on such a database, a machine learning model can be trained to predict simulation results. As an example, one or more application programming interfaces (APIs) can be built and implemented so that an application or framework can readily query a mud motor engine (e.g., a computational engine).

As an example, a method can include generating various types of information such as, for example, the difference between mud motor torque and top drive torque may be utilized to evaluate torque loss within a drillstring. Also, downhole rotation mechanical specific energy (MSE) can be calculated by plugging in the motor torque to MSE equation, which will be useful to estimate bit wear. MSE is a metric that can describe the energy spent in removing a unit volume of rock/formation mass.

As an example, a system can provide for utilization of hardware, sensors, data and modeling to deliver an answer product that can to be applied to post run analysis and/or real-time analysis, which may include output of instructions, recommendations, control signals, etc.

As an example, a system can include utilization of a large number of downhole and surface data sets. As an example, a data analytics platform such as, for example, the DATAIKU platform, etc., may be used to create an analysis workflow (e.g., on thousands of field runs, etc.). Through data analytics and data mining, output of an analysis can be applied in offset well analysis in well planning (e.g. drill planning, etc.) to better match a bit mud motor system. As mentioned, a system may be implemented in real-time operation (e.g., drilling operations, etc.), for example, to assist decision making, control, etc., by monitoring motor efficiency, motor performance and degradation.

As an example, a system can provide for calculating motor efficiency; calculating motor degradation; determining motor efficiency ranges for certain applications; determining motor degradation ranges for certain applications and degradation upper limits; applying ranges in motor job planning; applying ranges in motor job monitoring (e.g., with associated graphical user interfaces, etc.); and/or applying ranges for motor predictive health monitoring, re-run recommendation, etc.

Drilling with positive displacement motors dominates oil field operation. In many cases, it provides operational and economic advantages over conventional rotary drilling. In US land drilling for example, many jobs using an RSS system use a configuration with a mud motor. Moreover, many wells in US land drilling are still using the traditional bit and motor combination to drill the curve and lateral. It's quite frequent to see in downhole measurement that the average bit RPM decreases over time without any major change in surface parameters. Since mud motor provides additional energy to the system, the decrease in RPM indicates the deterioration of motor efficiency. If such changes continue, it will likely to entail worsen drilling dynamics and surface/downhole equipment failures. It is beneficial to estimate the energy and efficiency of the mud motor based on available data, and to enable a way of monitoring the status of the mud motor.

An analysis method has been developed to evaluate mud motor energy and efficiency and to estimate the mud motor degradation. With the surface drilling parameter and downhole rotational speed synchronized, the flow rate, mud motor rotational speed and differential pressure are obtained from the measured data. By utilizing mud motor power characteristic curves, the mud motor torque and power output are calculated, thus the mud motor efficiency can be computed from the ratio of motor power input to power output. In addition, an alternative equation can be used to incorporate the mud motor off bottom pressure to calculate the motor efficiency. The change rate of the motor efficiency is also obtained by fitting the time-based or depth-based efficiency results. Meanwhile, a mud motor degradation indicator is developed to estimate how much the mud motor has degraded over time. The degradation indicator is based on calculating the difference between the measured mud motor rotational speed and the nominal rotational speed based on the motor characteristic curve. Also, by fitting the calculated degradation indicator over drilling time or drilling depth, the degradation rate can be estimated.

The motor characteristic curves can play an important role in the method. Two approaches can be used to obtain the curves. The first approach is to use the mud motor specification library where the pre-defined mud motor curves are stored. The other approach is to utilize a mud motor engine trained using machine learning based on mud motor power section FEA modeling results. With the mud motor torque, differential pressure and power output being available, the method can calculate the torque rating, differential pressure rating and power rating, hence to evaluate whether the motor is working in an optimum zone.

The method can also generate other useful information. For instance, the difference between the mud motor torque and top drive torque can be used to evaluate the torque loss within the drill string. Also, downhole rotation MSE can be calculated by plugging in the motor torque to MSE equation, which can be useful to estimate bit wear.

This analysis method leverages the hardware, sensors, data and modeling to deliver an answer product that can to be applied to post run analysis as well as in real-time. The analysis method can be applied to massive number of downhole and surface data sets. A cloud based data analytics platform can be used to create an analysis workflow and apply the method on thousands of field runs. Through data analytics and data mining, the output of the analysis can be applied in offset well analysis in well planning to better match the bit mud motor system. The method can be implemented in real-time operation to assist decision making by monitoring the motor efficiency, motor performance and degradation.

This approach can be used for calculating motor efficiency, calculating motor degradation, determining motor efficiency ranges for certain applications, determining motor degradation ranges for certain applications and degradation upper limits, applying the ranges in motor job planning, applying the ranges in motor job monitoring (including the UI), and applying the ranges for motor predictive health monitoring, and re-run recommendation.

As an example, a system can provide for one or more real-time mud motor efficiency metrics, which can include, for example, one or more degradation metrics. As explained, a system can acquire real-time data, compare performance with predicted performance based on estimated degradation (e.g., wear, etc.), and update expected performance (e.g., efficiency, life, etc.) for a motor as it is utilized in drilling.

FIGS. 7 and 8 show examples of graphical user interfaces (GUIs) 700, 800 and 810. The GUIs 700, 800 and 810 include plots of examples of data measurements associated with motor drilling. The plots illustrate a tendency for downhole ROP to decrease even when surface parameters remain relatively constant.

In the examples of FIGS. 7 and 8 , a motorized RSS BHA was used with CC_RPM and CCRPM_AV showing downhole rotational speed. As shown, the downhole RPM kept decreasing while surface RPM and flow rate was consistent, which means mud motor RPM must be decreasing. The decreasing of mud motor RPM indicates the loss of mud motor efficiency. Towards the end of the run, due to the efficiency decay, the mud motor is unable to sustain torque demanded, resulting in reduction of ROP and relatively severe stick-slip.

FIG. 9 shows an example of a GUI 900 that includes a plot pertaining to a power section specification from a mud motor engine. As shown, the torque slope changes by approximately 3 percent when temperature increases from 30 C to 90 C for a motor with 600 gpm flow.

As an example, a system can provide for motor input energy from hydraulics flow:

HP _(in) =ΔP· Flow

-   -   where ΔP is differential pressure and Flow is flow rate.

As an example, a system can provide for motor output energy given by:

HP _(out)= T · RPM _(m)

-   -   where T is motor torque output and RPM m is motor RPM.

As an example, a system can provide for motor torque T as approximated by:

T =ΔP _(a) ·TorqueSlope

-   -   where TorqueSlope can be obtained from a power section spec or         mud motor engine where ΔP_(a) is the active differential         pressure which contributes to torque generation.

As an example, motor efficiency can be given by:

$e = {\frac{HP_{out}}{HP_{in}} = {\frac{\overset{¯}{T} \cdot \overset{\_}{R⁢P⁢M_{m}}}{\overset{\_}{\Delta ⁢P} \cdot \overset{\_}{Flow}} = {\frac{\overset{\_}{\Delta ⁢P_{a}} \cdot {TorqueSlope} \cdot \overset{\_}{R⁢P⁢M_{m}}}{\overset{\_}{\Delta ⁢P} \cdot \overset{\_}{Flow}} = {\frac{\overset{\_}{\Delta ⁢P_{a}} \cdot {TorqueSlope} \cdot \overset{\_}{R⁢P⁢M_{m}}}{\left( {{\Delta P_{0}} + {\overset{\_}{\Delta ⁢P_{a}} \cdot}} \right) \cdot \overset{\_}{Flow}}\overset{{\Delta P_{0}} \cong 0}{\Rightarrow}\frac{Torqu{{eSlope} \cdot \overset{\_}{R⁢P⁢M_{m}}}}{\overset{\_}{Flow}}}}}}$

-   -   where ΔP₀ is the no load differential pressure can be obtained         from motor engine or specification.

FIG. 10 shows example GUIs 1000 and 1010 relating to efficiency. As shown in the GUI 1010, contours can exist as to efficiencies that may change with respect to time, drilling depth, etc. In such an example, the GUI 1010 may be rendered in real-time to show a current status and past status(es) along with predicted future status(es) such that one or more decisions may be made (e.g., manually, semi-automatically, automatically, etc.).

FIG. 11 shows an example GUI 1100 that includes a plot of RPM versus differential pressure where nominal and measured RPM are indicated via an equation for “R”, which is a degradation metric (e.g., R metric, R ratio, etc.). As shown, the nominal RPM can be represented by ω and the measured motor RPM can be represented by ω*. As shown, R can be defined as a difference between nominal and measured RPM (e.g., measured less than nominal) divided by nominal RPM. As shown, in the plot of the GUI 1100, for an operational differential pressure, a difference can exist between nominal and measured RPM, which may be for a particular flow rate (e.g., gallons per minute, etc.). As an example, performance of a power section (see, e.g., the power section 514 in FIG. 5 ) can depend on type of liner (e.g., type of elastomeric material, etc.) installed in the power section. As to a nominal RPM, it can be computed on based on conditions, etc., given specifications for a power section of a mud motor.

FIG. 12 shows examples of GUIs 1200 and 1210 that show two different degradation rates (e.g., 0.484 percent per hour and 0.641 percent per hour) where, for example, a linear fit can be utilized to obtain a motor degradation change rate. As an example, R metric values may be computed for data pairs of nominal and measured and the R metric values may be fit using a regression technique, etc. In the example GUIs 1200 and 1210, the degradation rate (e.g., degradation change rate) may be with respect to time and/or depth (e.g., distance, etc.).

FIG. 13 shows an example of a system 1300 that includes data blocks 1312 and 1314, a synchronization block 1320, a motor torque/RPM block 1330 and an energy block 1340. As shown, the blocks 1312, 1314, 1320, 1330 and 1340 can provide one or more outputs per an output block 1350. For example, consider bit/motor torque, drilling torque loss, motor power output, motor power input, bit/motor efficiency, power/max power, etc. Such a system may utilize one or more of the foregoing equations, mud motor engine, etc. In the example of FIG. 13 , an approach can acquire surface data and downhole data and synchronizes them automatically. Once the data sources are synchronized, the motor torque and RPM can be calculated and used to calculate energy.

FIG. 14 shows an example of a workflow 1400 where data may be collected from a variety of sources; for example, tool life data, dump files, real-time data captures, simulation results, and others may be captured as part of data collection. A variety of data preparation actions may be performed to clean the data. As noted above, data may also be synchronized. From such data, motor power and efficiency analysis may be performed where results may be rendered to one or more displays via one or more visualizations.

As an example, visualizations based on results may provide insights into which entities are operating with higher motor efficiency, which motors are less susceptible to efficiency decay, and which combinations of bit, motors, and other BHA components provide the most product configuration in particular circumstances.

As explained, a system, a method, a workflow, etc., may be configured to run in real-time. In such instances, real-time data channels may be utilized and, for example, a demand for synchronization may be less in such circumstances.

FIG. 15 shows example GUIs 1500 and 1510 as to motor type and motor size, respectively. Such motors may be utilized as part of a library of motors with parameters that may be, for example, generated at least in part using simulation data.

FIG. 16 shows an example of a GUI 1600 that shows various workflow elements that can be utilized in a data analytics platform (e.g., DATAIKU, etc.) that can provide for motor power and efficiency analysis. As shown, various elements in the workflow pertain to automatic state detection and reference connections, application of time synchronization (e.g., as may be appropriate for stored and/or live data), and mud motor power and efficiency indicators (e.g., efficiency with respect to depth, efficiency with respect to time, and/or one or more other efficiency indicators). In the example of FIG. 16 , various sources of data are on the left side and various outputs are on the right side.

FIG. 17 shows an example of a GUI 1700 that includes a table of motor power and efficiency, drilling mechanics, and performance indicators. Such a table may be considered a table of some examples of motor efficiency computation outputs.

FIG. 18 shows example GUIs 1800 and 1810, which include plots as to motor efficiency over depth drilled and/or time drilled. As shown, decay rates can vary.

FIG. 19 shows example GUIs 1900 and 1910 that correspond to the GUIs 1800 and 1810 of FIG. 18 , respectively. The GUIs 1900 and 1910 show efficiency decay statistics.

FIGS. 20, 21, 22 and 23 show various GUIs 2000, 2100, 2200 and 2300 that include plots relating to torque loss. As explained, motor torque can be computed using differential pressures and torque slopes of motor curves.

FIG. 24 shows example GUIs 2400 and 2410 as to motor run torsional MSE. In the examples of FIG. 24 , motor torque is computed with both surface and downhole MSE, for example, as follows:

${MSE} = {\frac{WOB}{A_{b}} + \frac{120 \cdot \pi \cdot {RPM} \cdot {TQ}}{{ROP} \cdot A_{b}}}$

FIG. 25 shows example GUIs 2500 and 2510 as to motor degradation with respect to depth drilled. As shown, motor degradation is inversely correlated with drilled length (e.g., measured depth, etc.).

FIGS. 26 and 27 show example GUIs 2600, 2700 and 2710 for motor degradation in the Midland Area in the State of Texas. Per the data, a majority of the runs show relatively low degradation rate (e.g., less than approximately 0.2 percent). As indicated in the distributions, an inverse correlation exists between degradation rate and drilled time.

FIGS. 28 and 29 show example GUIs 2800 and 2900 where an envelope may be defined, for example, using a regression analysis and a selected function (e.g., exponential, etc.). Such an approach can be utilized to determine a degradation limit or degradation limits. As an example, a method can include estimating motor life limit in terms of drilling time and/or drilling depth given degradation rate in real-time. Such an approach may be utilized to determine one or more parameters of a drilling operation (e.g., ROP, etc.). As an example, an envelope or region may be determined and utilized in one or more field operations, for example, to control drilling to maintain degradation below a particular degradation rate.

FIGS. 30 and 31 show example GUIs 3000, 3010 and 3100 as to motor degradation with field incident information. As shown, incident coding may be utilized such that information regarding incidents and drilling can be understood, controlled, etc. In FIG. 31 , a bar chart shows counts versus incident type.

FIG. 32 shows example GUIs 3200 and 3210 as to motor degradation and field incidents, along with pull out of hole (POOH) reasons. As an example, a system can provide for extracting runs with degradation rate no less than a desired limit such as, for example, 0.5%/100 ft. In FIG. 32 , two incident types with the highest counts are power drive and mud motor failure. As indicated, there is higher chance of motor failure where the degradation rate is higher than 1.0%/100 ft. As to the POOH reason, the highest counts were for downhole tool failure and steerable tool failure.

FIGS. 33 and 34 show example GUIs 3300 and 3400 for a particular motor where failure occurred. In particular, motor degradation rate accelerated at the end of the run where the mud motor eventually lost power and could not spin. In such an example, a possibility may have existed to save the mud motor, for example, if a call was made before the degradation rate accelerated. As an example, a real-time system can provide for indications of one or more types of issues, which may be utilized for control, alarms, etc. In another example for another motor, a relatively high motor degradation rate existed where, eventually, the mud motor lost differential pressure and the drive shaft broke.

FIGS. 35 and 36 show example GUIs 3500 and 3600 for a particular motor where pressure loss occurred. In such an example, there was a possible washout in the BHA.

FIGS. 37 and 38 show example GUIs 3700 and 3800 for a particular motor where a failure occurred. In the example of FIGS. 37 and 38 , the BHA showed a 45 degree washed crack, indicating high frequency torsional oscillation (HFTO). HFTO can create cyclic fatigue loading and limit tool life and drilling performance. As an example, a system can provide for mitigation of HFTO, for example, by considering rock drilled, bit design, mud motor, mechanics of RSS and other tools in a BHA, as well as drilling parameters. Such a system can help to reduce premature drilling component failure and improve drilling performance, especially in high energy drilling applications (e.g., North America land, etc.). In the example of FIGS. 37 and 38, the bit box being free to rotate at POOH indicates motor failure. The degradation of the mud motor may have led to HFTO. In such an example, one or more indicators, alarms, control signals, etc., may be issued by a system to mitigate degradation of a mud motor, which may help to reduce one or more effects of HFTO (e.g., as to other components, a borehole, etc.).

FIG. 39 shows an example of GUI 3900 that includes a degradation distribution plot for cumulative distribution per degradation. As an example, a system may provide guidelines for motor degradation that can issue one or more alerts in real-time. For example, consider thresholds of greater than or equal to 20 percent as being high degradation and greater than or equal to 30 percent as being severe degradation. As indicated in the GUI 3900, P80 and P90 may be utilized to determine one or more thresholds (e.g., as to acceptable, high, severe, etc.).

FIG. 40 shows an example of GUI 4000 that includes a degradation distribution plot for cumulative distribution per degradation. As an example, a system may provide guidelines for motor degradation that can issue one or more alerts in real-time. For example, consider thresholds of greater than or equal to 20 percent as being high degradation and greater than or equal to 30 percent as being severe degradation. As indicated in the GUI 4000, P80 and P90 may be utilized to determine one or more thresholds (e.g., as to acceptable, high, severe, etc.).

FIGS. 41 and 42 show example GUIs 4100 and 4200 as to degradation rate distribution. As shown in the GUI 4200, P80 and P90 (e.g., or other metrics) may be utilized to determine one or more thresholds in terms of degradation rate, for example, in terms of percent per amount of distance and/or per amount of time (e.g., drilling time). As shown, P80 corresponds to approximately 0.3 percent per 100 ft and P90 corresponds to approximately 0.8 percent per 100 ft.

FIG. 43 shows an example GUI 4300 that includes a degradation change distribution plot that is based on start and end data. For example, degradation start and end can be defined as below:

-   -   If Depth Drilled >900 ft (10 std): Degradation Start=average         degradation of first 90 ft drilling, Degradation End=average         degradation of last 90 ft; or     -   If Depth Drilled<900 ft: Degradation Start=average degradation         of first 10% of depth drilled, Degradation End=average         degradation of 10% of depth drilled.

FIG. 44 shows an example GUI 4400 that includes a mud motor efficiency distribution plot. As shown, one or more probabilistic and/or statistical techniques may be utilized, for example, the GUI 4400 shows that P50 corresponds to an efficiency of approximately 76 percent.

As an example, a system may be utilized in a bit and motor planning operation (e.g., a planning framework, etc.). As an example, a system may be utilized in combination with a dynamic drilling interpretation framework (e.g., consider the TECHLOG framework, etc.). As an example, a system may be utilized in real-time, for example, for motor efficiency and decay monitoring (e.g., consider integration with a drilling operations framework). As an example, a system may be utilized for mud motor health analysis. As explained, one or more probabilistic and/or statistical techniques may be utilized for analysis. As an example, one or more data-driven approach may be utilized, for example, consider one or more machine learning techniques. As an example, a machine learning technique may be utilized to generate a trained machine learning model that may be operable in real-time to provide real-time guidance, control, decision making, etc., capabilities of a system.

FIG. 45 shows an example of a GUI 4500 of a system where the GUI 4500 provides various graphical controls, fields, visualizations, etc., for bit and motor runs, which can include historical runs, real-time runs, etc. Such a GUI may be utilized for planning, forensics, operations, etc. In FIG. 45 , the GUI 4500 includes a map with wellsite locations, which can be for actual wells and/or planned wells. As shown, a distance control can provide for ranging of the map, for example, to provide for selection, visualization, etc., of one or more wellsites. As shown, for a map region, various runs can be listed along with details. In such an example, offset well analyses may be performed for a target well, which may be in planning, development, operation, etc. The GUI 4500 shows a field with a number of runs that may exist within a map region, etc. As an example, a well may include one or more branches. As an example, multiple wells may be drilled from a common pad.

In the example of FIG. 45 , the GUI 4500 includes a ranking panel with a ranking of motor and bit combinations. As shown, various graphical controls may be utilized for filtering, searching, etc., information pertaining to motors, bits, motor and bit combinations, etc. For example, consider one or more graphical controls as to one or more of bore diameter, BHA type, bit model, bit manufacturer, year, company (e.g., drilling company, owner, etc.).

FIG. 46 shows an example of a GUI 4600 of a system where the GUI 4600 includes various graphics associated with bits and motors. Such a GUI may be utilized for performing planning, operations, etc. For example, consider performing an offset well data analysis to identify runs, better efficiency runs and operating parameter ranges, etc. As an example, the IDEAS framework (Schlumberger Limited, Houston, Texas) can provide for modeling and simulations as to drillstring performance (e.g., rate of penetration, etc.), for example, for comparisons of performance of bit and motor combinations. As shown, various bits and various motors may be utilized in assessing combinations. As an example, a system can provide for bit and motor matching, which may provide for consideration of one or more of highest ROP, most stable drilling, balanced motor efficiency and durability, etc.

As an example, a system can include one or more controllers and may be referred to as an autodriller system or an “AutoROP” system or a “ROPO” system (rate of penetration optimization system). For example, consider a weight on bit (WOB) controller, a drilling torque (TQA) controller, a differential pressure (DIFF_P) controller and a rate of penetration (ROP) controller. Each of the controllers may receive a corresponding set point (SP) value where each of the controllers receives a measured value (e.g., a WOB measurement, a TQA measurement and a DIFF_P measurement, respectively). Each of the controllers may output a normalized (NM) value (e.g., scaled from 0 to 1, etc.) that is received by the ROP controller where the ROP controller can utilize the normalized (NM) values and a ROP set point (SP) value to generate a ROP output. As an example, such a system can be operatively coupled to and/or include a degradation and/or efficiency system (e.g., a degradation and/or efficiency engine, framework, etc.) where, for example, control signals for drilling may be based at least in part on one or more of degradation and efficiency of a mud motor, where a mud motor is utilized (e.g., as part of a drillstring). As an example, sliding mode and/or rotating mode decisions and/or operations may be based at least in part on one or more of degradation and efficiency. Such decisions and/or operations may aim to maintain sufficient life in a power section of a mud motor to complete a run without having to pull a drillstring out of hole (POOH) for servicing, etc. Such an approach can help to reduce non-productive time (NPT) during drilling operations for one or more wells.

As an example, an agent may be trained to provide for output as to one or more of WOB, TQA, DIFF_P, ROP, etc. For example, such an agent may be part of a ROP system where output of the agent guides drilling to achieve a desirable ROP.

As an example, a system can include features for prediction of propagation direction of a drill bit, which may be operatively coupled to a mud motor, based on forces and bit and/or motor characteristics. Such a system may utilize a computational framework that includes one or more features of a framework such as, for example, the IDEAS framework. The IDEAS framework utilizes the finite element method (FEM) to model various physical phenomena, which can include reaction force at a bit (e.g., using a static, physics-based model). The FEM utilizes a grid or grids that discretize one or more physical domains. Equations such as, for example, continuity equations, are utilized to represent physical phenomena. The IDEAS framework, as with other types of FEM-based approaches, provides for numerical experimentation that approximates real-physical experimentation. In various instances, a framework can be a simulator that performs simulations to generation simulation results that approximate results that have occurred, are occurring or may occur in the real-world. In the context of drilling, such a framework can provide for execution of scenarios that can be part of a workflow or workflows as to planning, control, etc. As to control, a scenario may be based on data acquired by one or more sensors during one or more well construction operations such as, for example, directional drilling. In such an approach, determinations can be made using scenario result(s) that can directly and/or indirectly control one or more aspects of directional drilling. For example, consider control of sliding and/or rotating as modes of performing directional drilling.

FIGS. 47 and 48 show example GUIs 4700 and 4800 that include various types of information with respect to time, which may be a proxy for depth. As example, the GUIs 4700 and 4800 can be part of a data workflow integration that can provide for measured bit RPM for mud motor runs, etc. As an example, a motor efficiency computation workflow can be integrated with a data workflow for downhole data and/or other data acquisition. As an example, a system may receive data from one or more downhole components, which may include one or more of accelerometers, gyroscopes, location circuitry (e.g., GPS, etc.), etc.

FIG. 49 shows an example of a GUI 4900 of a data acquisition framework (e.g., consider a TECHLOG plugin framework, etc.). As shown, a graphical control for mud motor interpretation may be provided for actuating one or more mud motor and/or bit analysis, control, etc., components.

FIG. 50 shows an example of a system 5000 and a GUI 5010. As shown, the system 5000 can be described in terms of an architecture for real-time mud motor health monitoring, which may be operatively coupled to field equipment for purposes of decision making, control, etc. As shown, various inputs can be field-based such as via one or more Internet of things (IoT) components. Data may be acquired via one or more systems, databases, etc. (e.g., QTRAC, MySQL, QUEST, Performance Took Kit (PTK), INTERACT, etc.).

As shown, processing can provide for processing of data such as, for example, temperature, mud flow, standpipe pressure (SPP), status(es) (e.g., bit on bottom, etc.), hole depth (e.g., measured depth, etc.), time, etc. As indicated, specific properties for each power section can be utilized along with, for example, one or more physics-based models of a power section and/or one or more data-driven approaches that utilize one or more machine learning techniques (e.g., one or more machine learning models, etc.). As shown, the system 5000 can include a surrogate reduced order model of a mud motor power section. Such an approach may function to provide for fatigue analysis for a mud motor power section.

As shown, the system 5000 can utilize cloud-based resources. Such a system may operate in real-time to output results (e.g., degradation results, etc.), which may be utilized in a feedback manner to field operations at one or more wellsites. As indicated, a result may be a remaining useful life (RUL), which can be based at least in part on degradation results. As shown in the example GUI 5010, a plot may be rendered for RUL during drilling with respect to time and/or depth. As shown, a particular power section can be identified along with a type of mud motor material (e.g., a type of rubber, etc.). As explained, degradation of an elastomeric material during drilling may result in a decrease in RUL. As explained, integrity of elastomeric material of a mud motor can depend on various factors. As indicated, wellsite data can be provided in real-time where such data can be processed using one or more types of models along with specific properties of a power section in real-time to provide real-time output (e.g., results, etc.), which can be utilized in control of real-time operations at a wellsite.

As explained with respect to various GUIs, one or more types of possible degradation and/or failure mechanisms may be identified using results of a system such as the system 5000 of FIG. 50 . In such an approach, one or more notifications may be issued as to one or more actions that may be taken at a wellsite to address a trend as to possible degradation, failure, etc. In such an example, a drilling operation may be controlled such that one or more goals are met such as, for example, reaching an end of a planned drilling run (e.g., a desired measured depth, etc.) before having to pull a drillstring out of the hole (POOH). As explained, an unplanned trip (e.g., POOH followed by RIH), can result in non-productive time (NPT). In general, drilling operations aim to minimize NPT.

As an example, a mud motor can be re-used, from run-to-run, whether for a common hole, another hole, etc. In such an example, where reuse occurs, maintenance may be performed on the mud motor, which may involve replacement of elastomeric material. As explained, elastomeric material may degrade or fail during a run. Where degradation is monitored during a run, one or more decisions may be made (e.g., at surface) in preparation of a subsequent run. For example, a decision may be made as to whether maintenance and/or replacement of elastomeric material is/are to occur before a subsequent run or whether another mud motor is to be utilized for the subsequent run. As explained, condition of elastomeric material may be relevant to drillstring behavior such as, for example, vibrational behavior, oscillatory behavior, etc., as a mud motor includes a rotating component or rotating components that can give rise to various types of drillstring behaviors.

FIG. 51 shows an example of a GUI 5100 of a system such as, for example, a drilling operations framework. As shown in FIG. 51 , the GUI 5100 can include various graphics, graphical controls, etc., as to drilling operations such as, for example, depth (e.g., actual and planned), toolface angle, differential pressure, weight on bit (WOB), flow rate (e.g., mud flow rate), RPM, etc. As shown, various types of information such as hookload, standpipe pressure and torque may be rendered in such a GUI.

FIG. 52 shows various examples of GUIs 5200, which include a RPM GUI 5210, a torque GUI 5220, a motor degradation GUI 5230, a motor power GUI 5240, a motor differential pressure GUI 5250 and a motor efficiency GUI 5260. Such GUIs can be part of a system such as, for example, the system 5000 of FIG. 50 , etc. As shown, various operational limits, thresholds, etc., may be set in the GUIs 5200 that can provide for visualization of operations, issuance of notifications, control signals, alarms, etc.

As shown in the GUI 5230, a high degradation ratio (see, e.g., the metric “R”) can be set that can correspond to a pull out of hole (POOH) signal. For example, if the degradation ratio reaches the indicated limit, a notification can be issued to stop drilling and to pull the drillstring out of the bore for servicing the mud motor (e.g., replacing a liner, replacing the mud motor, replacing the mud motor and the bit, etc.). The GUI 5230 can provide a range of degradation where a low degradation may indicate that drilling can be more aggressive if appropriate and where a higher degradation may indicate that drilling can be less aggressive if appropriate (e.g., to conserve life of the mud motor to increase probability of completing a drilling run, etc.).

As shown in the GUI 5260, motor efficiency can be rendered utilizing one or more graphics where an operator, controller, etc., may aim to operate within a particular efficiency range (e.g., 30 percent to 60 percent). In the GUIs 5230 and 5260, current motor degradation and current motor efficiency may be rendered such that an operator, controller, etc., can be aware of one or more relationships between degradation and efficiency such that one or more tradeoffs may be made during drilling operations.

As explained, degradation and/or efficiency may depend on location, type of formation, etc., such that results may be associated with particular locations (e.g., particular fields, basins, etc.). As an example, a system may provide differences in motor efficiencies and/or degradations at different locations along with different motor models, etc.

As mentioned, motor torque may be calculated using different pressure and torque slope of a motor curve. As an example, mechanical specific energy (MSE) can be calculated at the surface and/or downhole/torsional MSE. In such an example, MSE may be split into a surface and a downhole component. As an example, a system may provide for estimates of motor power output, which may be with respect to time, depth, etc. As explained, a system can provide for degradation rate and one or more predictions as to life of a motor, which may include providing an estimate of when the motor may fail. As explained, one or more notifications may be issued as to field operations that can be taken to increase life to assure that a motor reaches an end of a run. For example, clustering of degradation change rates may be utilized for various runs where such clusters can be used to advise how long or how far an engineer (e.g., or an automated drilling system) can drill before a change in mud motor or operation thereof is likely be demanded.

As explained, a motor efficiency analysis can be used in real-time to provide recommendations for motor changes, tripping, and/or provide more accurate estimations for time and depth. Such an analysis may be used to help plan wells by allowing planners to do bit-motor offset analysis and select the best bit-motor-BHA combination for a drillstring for a particular well based on performance of offset wells. Such an approach can account for formation characteristics (e.g., lithology, etc.) and may account for factors such as vibration, oscillation, etc. As an example, a method may account for borehole integrity such that a sufficiently sturdy borewall is formed that is not likely to collapse.

As an example, a method can include performing simulations, which may provide results to quality control estimates, to supplement estimates, to enhance estimates, etc. For example, consider a system that can output a remaining life of a power section of a mud motor with a particular bit where bit wear, etc., may be taken into account. In such an example, where an issue may exist as to an ability to successfully complete a run, a simulator may be utilized to perform simulations to double check the remaining life, an ability to complete the run, etc. Such an approach may utilized cloud-based resources where simulation results may or may not be provided in real-time or near real-time. For example, consider a simulation that can run in about 30 minutes as to a future state of a drilling operation that may be expected to be encountered in an amount of time that is greater than 30 minutes. In such an example, real-time results of remaining life as to future drilling can be checked via detailed simulation where the detailed simulation results are available without having to halt drilling (e.g., encounter NPT, etc.) and in advance of a future drilling state where one or more control actions may be taken to enhance remaining life, if appropriate.

As explained, a system can provide for implementation of a real-time efficiency workflow. Such a workflow may generate instantaneous motor power, torque, and efficiency and illustrate an optimization range. For example, one or more of the GUIs of FIGS. 51 and 52 may be utilized to show real-time and track history of the motor torque, motor efficiency, and motor power and/or to set one or more warning thresholds for motor efficiency, motor power, differential pressure, and a motor degradation indicator. As explained, one or more GUIs may show one or more degradation status indicators and provide an indicator for a driller (e.g., human, machine, etc.) when degradation jeopardizes one or more objectives and/or performance indicators (PIs).

As explained, the GUI 4500 of FIG. 45 can provide for performing bit-motor offset analysis as part of an offset well analysis at a planning stage and/or provide for one or more analyses in real-time. As explained, a system can provide output such as an indicator as to when (e.g., time and/or depth) a trip out (POOH) is to occur, for example, to replace and/or service a mud motor and/or a bit. Such a system can provide information on parameter optimization for a bit and motor system (e.g., for planning, replacement, etc.). As an example, information from one run for a section of a well may inform decision making as to another run for the same section or another section of the well. In such an approach, engineers may have a better understanding of which combinations of mud motors and bits are most efficient for drilling operations (see, e.g., the GUI 4600 of FIG. 46 , etc.).

As explained, a mud motor health analyzer may be used for real-time mud motor health monitoring. For example, in FIG. 50 , the system 5000 can provide an indicator that can inform a GUI such that a red, yellow and/or green light(s) may be shown as output for a human where red indicates poor health, yellow indicates an intermediate health and green indicates good health. As explained, an indication may be provided as to whether, for example, a motor can be re-run, whether the motor is at the end of its life, or an expected remaining life for the motor. As an example, a system may recommend servicing of a mud motor by replacing a liner with a same type of liner or a different liner (e.g., a different elastomeric material, etc.).

As an example, a system can provide for computing motor degradation, motor efficiency, motor remaining life from drilling data, motor efficiency range from drilling data, etc. As an example, such a system may include and/or be operatively coupled to a visualization framework that can call for rendering of one or more GUIs. For example, consider the ability to format of displaying degradation in real-time, which may utilize a time history, a box whisker, a dial plot, etc. In such examples, one or more GUIs may provide risk level indicators, which may be based target well drilling and/or offset drilling data, for example, to issue an alert as to motor severe degradation. As an example, a system may provide for formatting of displays for efficiency in real-time (e.g., time history, box whisker, dial plot, etc.), which may include risk levels based on target well drilling and/or offset drilling data (e.g., to recommend appropriate drilling parameters, etc.).

As explained, a system may provide for selections, recommendations, etc., as to one or more drilling parameters (e.g., consider a parameter advisory system, etc.). As explained, a system may provide for manual, semi-automated and/or automated control. For example, a system may be operatively coupled to an AutoROP system, etc.

FIG. 53 shows an example of a method 5300 and an example of a system 5390. In the example of FIG. 53 , the method can include a reception block 5310 for receiving real-time data during a drilling operation performed by a drillstring that includes a mud motor and a bit characterized by an expected performance profile; a determination block 5320 for determining actual performance of the drillstring based at least in part on the real-time data; a prediction block 5330 for predicting degraded performance of the drillstring based at least in part on the real-time data and a mud motor degradation model; and an update block 5340 for updating the expected performance profile based on a comparison of the actual performance and the degraded performance.

In the example of FIG. 53 , the system 5390 includes one or more information storage devices 5391, one or more computers 5392, one or more networks 5395 and instructions 5396. As to the one or more computers 5392, each computer may include one or more processors (e.g., or processing cores) 5393 and memory 5394 for storing the instructions 5396, for example, executable by at least one of the one or more processors. As an example, a computer may include one or more network interfaces (e.g., wired or wireless), one or more graphics cards, a display interface (e.g., wired or wireless), etc.

The method 5300 is shown along with various computer-readable media blocks 5311, 5321, 5331 and 5341 (e.g., CRM blocks). Such blocks may be utilized to perform one or more actions of the method 5300. For example, consider the system 5390 of FIG. 53 and the instructions 5396, which may include instructions of one or more of the CRM blocks 5311, 5321, 5331 and 5341.

As mentioned, one or more machine learning techniques may be utilized to enhance process operations, a process operations environment, a communications framework, etc. As explained, various types of information can be generated via operations of a communications framework where such information may be utilized for training one or more types of machine learning models to generate one or more trained machine learning models, which may be deployed within one or more frameworks, environments, etc.

As to types of machine learning models, consider one or more of a support vector machine (SVM) model, a k-nearest neighbors (KNN) model, an ensemble classifier model, a neural network (NN) model, etc. As an example, a machine learning model can be a deep learning model (e.g., deep Boltzmann machine, deep belief network, convolutional neural network, stacked auto-encoder, etc.), an ensemble model (e.g., random forest, gradient boosting machine, bootstrapped aggregation, AdaBoost, stacked generalization, gradient boosted regression tree, etc.), a neural network model (e.g., radial basis function network, perceptron, back-propagation, Hopfield network, etc.), a regularization model (e.g., ridge regression, least absolute shrinkage and selection operator, elastic net, least angle regression), a rule system model (e.g., cubist, one rule, zero rule, repeated incremental pruning to produce error reduction), a regression model (e.g., linear regression, ordinary least squares regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, logistic regression, etc.), a Bayesian model (e.g., naïve Bayes, average on-dependence estimators, Bayesian belief network, Gaussian naïve Bayes, multinomial naive Bayes, Bayesian network), a decision tree model (e.g., classification and regression tree, iterative dichotomiser 3, C4.5, C5.0, chi-squared automatic interaction detection, decision stump, conditional decision tree, M5), a dimensionality reduction model (e.g., principal component analysis, partial least squares regression, Sammon mapping, multidimensional scaling, projection pursuit, principal component regression, partial least squares discriminant analysis, mixture discriminant analysis, quadratic discriminant analysis, regularized discriminant analysis, flexible discriminant analysis, linear discriminant analysis, etc.), an instance model (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, locally weighted learning, etc.), a clustering model (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, etc.), etc.

As an example, a machine model may be built using a computational framework with a library, a toolbox, etc., such as, for example, those of the MATLAB framework (MathWorks, Inc., Natick, Massachusetts). The MATLAB framework includes a toolbox that provides supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor (KNN), k-means, k-medoids, hierarchical clustering, Gaussian mixture models, and hidden Markov models. Another MATLAB framework toolbox is the Deep Learning Toolbox (DLT), which provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. The DLT provides convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. The DLT includes features to build network architectures such as generative adversarial networks (GANs) and Siamese networks using custom training loops, shared weights, and automatic differentiation. The DLT provides for model exchange various other frameworks.

As an example, the TENSORFLOW framework (Google LLC, Mountain View, CA) may be implemented, which is an open source software library for dataflow programming that includes a symbolic math library, which can be implemented for machine learning applications that can include neural networks. As an example, the CAFFE framework may be implemented, which is a DL framework developed by Berkeley AI Research (BAIR) (University of California, Berkeley, California). As another example, consider the SCIKIT platform (e.g., scikit-learn), which utilizes the PYTHON programming language. As an example, a framework such as the APOLLO AI framework may be utilized (APOLLO.AI GmbH, Germany). As an example, a framework such as the PYTORCH framework may be utilized (Facebook AI Research Lab (FAIR), Facebook, Inc., Menlo Park, California).

As an example, a training method can include various actions that can operate on a dataset to train a ML model. As an example, a dataset can be split into training data and test data where test data can provide for evaluation. A method can include cross-validation of parameters and best parameters, which can be provided for model training.

The TENSORFLOW framework can run on multiple CPUs and GPUs (with optional CUDA (NVIDIA Corp., Santa Clara, California) and SYCL (The Khronos Group Inc., Beaverton, Oregon) extensions for general-purpose computing on graphics processing units (GPUs)). TENSORFLOW is available on 64-bit LINUX, MACOS (Apple Inc., Cupertino, California), WINDOWS (Microsoft Corp., Redmond, Washington), and mobile computing platforms including ANDROID (Google LLC, Mountain View, California) and IOS (Apple Inc.) operating system based platforms.

TENSORFLOW computations can be expressed as stateful dataflow graphs; noting that the name TENSORFLOW derives from the operations that such neural networks perform on multidimensional data arrays. Such arrays can be referred to as “tensors”.

As an example, a device may utilize TENSORFLOW LITE (TFL) or another type of lightweight framework. TFL is a set of tools that enables on-device machine learning where models may run on mobile, embedded, and IoT devices. TFL is optimized for on-device machine learning, by addressing latency (no round-trip to a server), privacy (no personal data leaves the device), connectivity (Internet connectivity is demanded), size (reduced model and binary size) and power consumption (e.g., efficient inference and a lack of network connections). Multiple platform support, covering ANDROID and iOS devices, embedded LINUX, and microcontrollers. Diverse language support, which includes JAVA, SWIFT, Objective-C, C++, and PYTHON. High performance, with hardware acceleration and model optimization. Machine learning tasks may include, for example, image classification, object detection, pose estimation, question answering, text classification, etc., on multiple platforms.

As an example, a system can provide for one or more real-time mud motor efficiency metrics, which can include, for example, one or more degradation metrics. As explained, a system can acquire real-time data, compare performance with predicted performance based on estimated degradation (e.g., wear, etc.), and update expected performance (e.g., efficiency, life, etc.) for a motor as it is utilized in drilling.

As an example, an expected performance profile can be generated during a planning phase of a drilling operation. For example, a planning framework may utilize mud motor specifications along with conditions for drilling that aim to provide a desired wellbore according to a desired wellbore trajectory. In such an example, an expected performance profile may be account for mud motor degradation or not. As an example, an expected performance profile may account for mud motor wear and/or bit wear and/or combined mud motor and bit wear. As an example, a system that can predict degraded performance may be utilized in planning, for example, to generate a more accurate expected performance profile (e.g., a profile with respect to depth and/or time). As explained, during an actual drilling operation, real-time data can be acquired such that predictions as to degraded performance can be more relevant than predictions in a planning phase (e.g., as may be based in part on simulated drilling, etc.). Thus, a real-time system for prediction of degraded performance can improve drilling operations beyond that provided by planning alone. As explained, with real-time predictions of degraded performance, drilling operations may be controlled in real-time for one or more purposes (e.g., to reach a target, to reach an end of a run, to reduce risk of failure, to reduce NPT, etc.).

As an example, an expected performance profile may be updated during drilling operations such that it is continually refined. At the end of a drilling run, such a performance profile may mirror actual performance and may provide insights as to improvements to performance, for example, for one or more additional runs in the same hole and/or in another hole (e.g., at least in part drilled or to be drilled).

As an example, a method can include receiving real-time data during a drilling operation performed by a drillstring that includes a mud motor and a bit characterized by an expected performance profile; determining actual performance of the drillstring based at least in part on the real-time data; predicting degraded performance of the drillstring based at least in part on the real-time data and a mud motor degradation model; and updating the expected performance profile based on a comparison of the actual performance and the degraded performance. In such an example, the mud motor degradation model can predict efficiency of the mud motor. For example, FIG. 50 shows the system 5000 as including a surrogate reduced order model of a mud motor power section where components can include one or more physics-based models and one or more data-driven models (e.g., one or more machine learning models). In such an example, degradation of a mud motor can be predicted, which can be an indication of fatigue (e.g., wear, etc.).

As explained, data from various drilling runs indicates that a correlation can exist between drilled length and motor degradation, which can be specific to particular equipment, fields, etc. As explained, various data can be utilized, which may be indicative of energy, power, etc. As explained, computed nominal RPM of a mud motor and measured RPM of a mud motor may be utilized in determining various performance metrics. Where a drilling run is expected to drill a particular distance (e.g., length) with a mud motor, degradation may alter such expectations. As explained, a system can provide for predictions of degradation (e.g., degraded performance, etc.) in real-time, which can inform drilling, particularly as to whether or not the drilling run can be completed (e.g., reach the particular distance) with a current mud motor (e.g., or mud motor and bit combination).

As an example, a mud motor degradation model can accounts for degradation of a liner of the mud motor (e.g., directly and/or indirectly). In such an example, the liner can include an elastomeric material.

As an example, a method can include predicting degraded performance in a manner that includes predicting a degradation rate. With a degradation rate, future degradation of performance may be estimated. As an example, a method can include accounting for past degradation (e.g., consider cumulative degradation, etc.).

As an example, a method can include generating a target range for degradation of a mud motor and/or generating a target range for efficiency of a mud motor.

As an example, a method can include, if degraded performance exceeds a degraded performance threshold, issuing a pull out of hole (POOH) notification. Such an approach can provide for removing a mud motor from a hole prior to failure of the mud motor (e.g., degradation to a point of inoperability, etc.). As explained, degradation may result in drillstring behaviors that can be detrimental (e.g., vibration, oscillations, etc.).

As an example, a method can include rendering a graphical user interface to a display that includes a mud motor degradation graphic and a mud motor efficiency graphic.

As an example, a method can include rendering a graphical user interface to a display that includes a graphic of remaining useful life of at least a mud motor versus time for a drilling operation.

As an example, a method can include rendering a graphical user interface to a display that includes a graphic of mud flow rate, differential pressure, mud motor efficiency and a current status that is based at least in part on real-time data.

As an example, a method can include predicting degraded performance by utilizing a computed nominal RPM of the mud motor and a measured RPM of the mud motor, where the measured RPM of the mud motor is less than the computed nominal RPM of the mud motor. In such an example, the computed nominal RPM and the measured RPM can be for an operational differential pressure. As an example, a measured RPM can be less than a computed nominal RPM due at least in part to degradation of the mud motor.

As an example, a method can include receiving real-time data that include surface data and downhole data.

As an example, a method can include issuing a control signal based at least in part on degraded performance. In such an example, issuing can issue a control signal to an automated rate of penetration controller (e.g., AutoROP controller, etc.).

As an example, a method can include, based at least in part on degraded performance, identifying a potential type of failure.

As an example, a system can include a processor; memory accessible to the processor; processor-executable instructions stored in the memory and executable by the processor to instruct the system to: receive real-time data during a drilling operation performed by a drillstring that includes a mud motor and a bit characterized by an expected performance profile; determine actual performance of the drillstring based at least in part on the real-time data; predict degraded performance of the drillstring based at least in part on the real-time data and a mud motor degradation model; and update the expected performance profile based on a comparison of the actual performance and the degraded performance.

As an example, one or more computer-readable media can include computer-executable instructions executable by a system to instruct the system to: receive real-time data during a drilling operation performed by a drillstring that includes a mud motor and a bit characterized by an expected performance profile; determine actual performance of the drillstring based at least in part on the real-time data; predict degraded performance of the drillstring based at least in part on the real-time data and a mud motor degradation model; and update the expected performance profile based on a comparison of the actual performance and the degraded performance.

As an example, a computer program product can include one or more computer-readable storage media that can include processor-executable instructions to instruct a computing system to perform one or more methods and/or one or more portions of a method.

In some embodiments, a method or methods may be executed by a computing system. FIG. 54 shows an example of a system 5400 that can include one or more computing systems 5401-1, 5401-2, 5401-3 and 5401-4, which may be operatively coupled via one or more networks 5409, which may include wired and/or wireless networks.

As an example, a system can include an individual computer system or an arrangement of distributed computer systems. In the example of FIG. 54 , the computer system 5401-1 can include one or more modules 5402, which may be or include processor-executable instructions, for example, executable to perform various tasks (e.g., receiving information, requesting information, processing information, simulation, outputting information, etc.).

As an example, a module may be executed independently, or in coordination with, one or more processors 5404, which is (or are) operatively coupled to one or more storage media 5406 (e.g., via wire, wirelessly, etc.). As an example, one or more of the one or more processors 5404 can be operatively coupled to at least one of one or more network interface 5407. In such an example, the computer system 5401-1 can transmit and/or receive information, for example, via the one or more networks 5409 (e.g., consider one or more of the Internet, a private network, a cellular network, a satellite network, etc.).

As an example, the computer system 5401-1 may receive from and/or transmit information to one or more other devices, which may be or include, for example, one or more of the computer systems 5401-2, etc. A device may be located in a physical location that differs from that of the computer system 5401-1. As an example, a location may be, for example, a processing facility location, a data center location (e.g., server farm, etc.), a rig location, a wellsite location, a downhole location, etc.

As an example, a processor may be or include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

As an example, the storage media 5406 may be implemented as one or more computer-readable or machine-readable storage media. As an example, storage may be distributed within and/or across multiple internal and/or external enclosures of a computing system and/or additional computing systems.

As an example, a storage medium or storage media may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLUERAY disks, or other types of optical storage, or other types of storage devices.

As an example, a storage medium or media may be located in a machine running machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.

As an example, various components of a system such as, for example, a computer system, may be implemented in hardware, software, or a combination of both hardware and software (e.g., including firmware), including one or more signal processing and/or application specific integrated circuits.

As an example, a system may include a processing apparatus that may be or include a general purpose processors or application specific chips (e.g., or chipsets), such as ASICs, FPGAs, PLDs, or other appropriate devices.

FIG. 55 shows components of an example of a computing system 5500 and an example of a networked system 5510 with a network 5520. The system 5500 includes one or more processors 5502, memory and/or storage components 5504, one or more input and/or output devices 5506 and a bus 5508. In an example embodiment, instructions may be stored in one or more computer-readable media (e.g., memory/storage components 5504). Such instructions may be read by one or more processors (e.g., the processor(s) 5502) via a communication bus (e.g., the bus 5508), which may be wired or wireless. The one or more processors may execute such instructions to implement (wholly or in part) one or more attributes (e.g., as part of a method). A user may view output from and interact with a process via an I/O device (e.g., the device 5506). In an example embodiment, a computer-readable medium may be a storage component such as a physical memory storage device, for example, a chip, a chip on a package, a memory card, etc. (e.g., a computer-readable storage medium).

In an example embodiment, components may be distributed, such as in the network system 5510. The network system 5510 includes components 5522-1, 5522-2, 5522-3, . . . 5522-N. For example, the components 5522-1 may include the processor(s) 5502 while the component(s) 5522-3 may include memory accessible by the processor(s) 5502. Further, the component(s) 5522-2 may include an I/O device for display and optionally interaction with a method. A network 5520 may be or include the Internet, an intranet, a cellular network, a satellite network, etc.

As an example, a device may be a mobile device that includes one or more network interfaces for communication of information. For example, a mobile device may include a wireless network interface (e.g., operable via IEEE 802.11, ETSI GSM, BLUETOOTH, satellite, etc.). As an example, a mobile device may include components such as a main processor, memory, a display, display graphics circuitry (e.g., optionally including touch and gesture circuitry), a SIM slot, audio/video circuitry, motion processing circuitry (e.g., accelerometer, gyroscope), wireless LAN circuitry, smart card circuitry, transmitter circuitry, GPS circuitry, and a battery. As an example, a mobile device may be configured as a cell phone, a tablet, etc. As an example, a method may be implemented (e.g., wholly or in part) using a mobile device. As an example, a system may include one or more mobile devices.

As an example, a system may be a distributed environment, for example, a so-called “cloud” environment where various devices, components, etc. interact for purposes of data storage, communications, computing, etc. As an example, a device or a system may include one or more components for communication of information via one or more of the Internet (e.g., where communication occurs via one or more Internet protocols), a cellular network, a satellite network, etc. As an example, a method may be implemented in a distributed environment (e.g., wholly or in part as a cloud-based service).

As an example, information may be input from a display (e.g., consider a touchscreen), output to a display or both. As an example, information may be output to a projector, a laser device, a printer, etc. such that the information may be viewed. As an example, information may be output stereographically or holographically. As to a printer, consider a 2D or a 3D printer. As an example, a 3D printer may include one or more substances that can be output to construct a 3D object. For example, data may be provided to a 3D printer to construct a 3D representation of a subterranean formation. As an example, layers may be constructed in 3D (e.g., horizons, etc.), geobodies constructed in 3D, etc. As an example, holes, fractures, etc., may be constructed in 3D (e.g., as positive structures, as negative structures, etc.).

Although only a few example embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the example embodiments. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims. In the claims, means-plus-function clauses are intended to cover the structures described herein as performing the recited function and not only structural equivalents, but also equivalent structures. Thus, although a nail and a screw may not be structural equivalents in that a nail employs a cylindrical surface to secure wooden parts together, whereas a screw employs a helical surface, in the environment of fastening wooden parts, a nail and a screw may be equivalent structures. 

1. A method comprising: receiving real-time data during a drilling operation performed by a drillstring that comprises a mud motor and a bit characterized by an expected performance profile; determining actual performance of the drillstring based at least in part on the real-time data; predicting degraded performance of the drillstring based at least in part on the real-time data and a mud motor degradation model; and updating the expected performance profile based on a comparison of the actual performance and the degraded performance.
 2. The method of claim 1, wherein the mud motor degradation model predicts efficiency of the mud motor.
 3. The method of claim 1, wherein the mud motor degradation model accounts for degradation of a liner of the mud motor, wherein the liner comprises an elastomeric material.
 4. The method of claim 1, wherein predicting the degraded performance comprises predicting a degradation rate.
 5. The method of claim 1, comprising generating a target range for degradation of the mud motor and/or generating a target range for efficiency of the mud motor.
 6. The method of claim 1, comprising issuing a pull out of hole (POOH) notification if the degraded performance exceeds a degraded performance threshold.
 7. The method of claim 1, comprising rendering a graphical user interface to a display that comprises a mud motor degradation graphic and a mud motor efficiency graphic.
 8. The method of claim 1, comprising rendering a graphical user interface to a display that comprises a graphic of remaining useful life of at least the mud motor versus time for the drilling operation.
 9. The method of claim 1, comprising rendering a graphical user interface to a display that comprises a graphic of mud flow rate, differential pressure, mud motor efficiency and a current status that is based at least in part on the real-time data.
 10. The method of claim 1, wherein predicting degraded performance comprises utilizing a computed nominal RPM of the mud motor and a measured RPM of the mud motor, wherein the measured RPM of the mud motor is less than the computed nominal RPM of the mud motor, wherein the computed nominal RPM and the measured RPM are for an operational differential pressure and wherein the measured RPM is less than the computed nominal RPM due at least in part to degradation of the mud motor.
 11. The method of claim 1, wherein the real-time data comprise surface data and downhole data.
 12. The method of claim 1, comprising issuing a control signal based at least in part on the degraded performance, optionally wherein the issuing issues the control signal to an automated rate of penetration controller.
 13. The method of claim 1, comprising, based at least in part on the degraded performance, identifying a potential type of failure.
 14. A system comprising: a processor; memory accessible to the processor; processor-executable instructions stored in the memory and executable by the processor to instruct the system to: receive real-time data during a drilling operation performed by a drillstring that comprises a mud motor and a bit characterized by an expected performance profile; determine actual performance of the drillstring based at least in part on the real-time data; predict degraded performance of the drillstring based at least in part on the real-time data and a mud motor degradation model; and update the expected performance profile based on a comparison of the actual performance and the degraded performance.
 15. The system of claim 14, wherein the mud motor degradation model predicts efficiency of the mud motor.
 16. The system of claim 14, wherein the mud motor degradation model accounts for degradation of a liner of the mud motor, wherein the liner comprises an elastomeric material.
 17. The system of claim 14, wherein the instructions include issuing a control signal based at least in part on the degraded performance, optionally wherein the issuing issues the control signal to an automated rate of penetration controller.
 18. A non-transitory computer-readable storage medium storing instructions that when executed by a computer, which includes a processor performs a method, the method comprising: receiving real-time data during a drilling operation performed by a drillstring that comprises a mud motor and a bit characterized by an expected performance profile; determining actual performance of the drillstring based at least in part on the real-time data; predicting degraded performance of the drillstring based at least in part on the real-time data and a mud motor degradation model; and updating the expected performance profile based on a comparison of the actual performance and the degraded performance.
 19. The non-transitory computer-readable storage medium of claim 18, wherein predicting degraded performance comprises utilizing a computed nominal RPM of the mud motor and a measured RPM of the mud motor, wherein the measured RPM of the mud motor is less than the computed nominal RPM of the mud motor, wherein the computed nominal RPM and the measured RPM are for an operational differential pressure and wherein the measured RPM is less than the computed nominal RPM due at least in part to degradation of the mud motor.
 20. The non-transitory computer-readable storage medium of claim 18, comprising issuing a control signal based at least in part on the degraded performance, optionally wherein the issuing issues the control signal to an automated rate of penetration controller. 